Prof. PhD DSc Eng
Leszek Rutkowski
Papers (200)
2025 (13)
Kaczmarek K., Dymova L., Sevastjanov P., Rutkowski L., Can volatility-dependent irregular forms of fuzzy local criteria increase the effectiveness of Forex trading models. (0)
Can volatility-dependent irregular forms of fuzzy local criteria increase the effectiveness of Forex trading models
, Can volatility-dependent irregular forms of fuzzy local criteria increase the effectiveness of Forex trading models, Expert Systems with Applications, 294, 294, 2025, Cites: 0
Min F., Xiao M., Cao J., Wang Z., Ding J., Rutkowski L., Spatiotemporal dynamics of a food chain model incorporating higher-order interactions and slow–fast effect. (0)
Spatiotemporal dynamics of a food chain model incorporating higher-order interactions and slow–fast effect
, Spatiotemporal dynamics of a food chain model incorporating higher-order interactions and slow–fast effect, Chaos Solitons and Fractals, 199, 199, 2025, Cites: 0
Cheng J., Xu Y., Rutkowski L., Yan H., Cao J., Zhang B., Qiu X., Observer-Based Security Control for 2-D Fuzzy Switched Systems With Nonhomogeneous Sojourn Probabilities. (1)
Observer-Based Security Control for 2-D Fuzzy Switched Systems With Nonhomogeneous Sojourn Probabilities
, Observer-Based Security Control for 2-D Fuzzy Switched Systems With Nonhomogeneous Sojourn Probabilities, IEEE Transactions on Cybernetics, 55, 55, 3332-3341, 2025, Cites: 1
Li H., Yao Y., Xiao M., Wang Z., Rutkowski L., Turing pattern dynamics in a fractional-diffusion oregonator model under PD control. (0)
Turing pattern dynamics in a fractional-diffusion oregonator model under PD control
, Turing pattern dynamics in a fractional-diffusion oregonator model under PD control, Nonlinear Analysis Modelling and Control, 30, 30, 291-311, 2025, Cites: 0
Cheng J., Liu N., Rutkowski L., Cao J., Yan H., Hua L., Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities. (7)
Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities
, Space-Time Sampled-Data Control for Memristor- Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities, IEEE Transactions on Circuits and Systems I Regular Papers, 72, 72, 1452-1461, 2025, Cites: 7
Xia Y., He J., Lam H.-K., Rutkowski L., Precup R.-E., Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism. (0)
Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism
, Non-fragile fuzzy control of input-saturated systems with global prescribed performance via an error-triggered mechanism, Information Sciences, 711, 711, 2025, Cites: 0
Kaczmarek K., Sevastjanov P., Dymova L., Kulawik A., Rutkowski L., A system of trading in the foreign exchange market based on multi-criteria optimization under Belief-Plausibility uncertainty. (0)
A system of trading in the foreign exchange market based on multi-criteria optimization under Belief-Plausibility uncertainty
, A system of trading in the foreign exchange market based on multi-criteria optimization under Belief-Plausibility uncertainty, Applied Soft Computing, 169, 169, 2025, Cites: 0
Xia Y., Xiao K., Cao J., Precup R.-E., Arya Y., Lam H.-K., Rutkowski L., Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance. (11)
Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance
, Stochastic Neural Network Control for Stochastic Nonlinear Systems With Quadratic Local Asymmetric Prescribed Performance, IEEE Transactions on Cybernetics, 55, 55, 867-879, 2025, Cites: 11
Xia Y., Xiao K., Cao J., Lam H.-K., Precup R.-E., Rutkowski L., Agarwal R.K., Customized Non-Monotonic Prescribed Performance Control for Stochastic MEMS Gyroscopes With Insufficient Input Capability. (0)
Customized Non-Monotonic Prescribed Performance Control for Stochastic MEMS Gyroscopes With Insufficient Input Capability
, Customized Non-Monotonic Prescribed Performance Control for Stochastic MEMS Gyroscopes With Insufficient Input Capability, IEEE Transactions on Circuits and Systems I Regular Papers, 2025, Cites: 0
Wang J., Yang Q., Cao J., Rutkowski L., Shen H., Reinforcement-Learning-Based Fuzzy Bipartite Consensus for Multiagent Systems: A Novel Scaling Off-Policy Learning Scheme. (0)
Reinforcement-Learning-Based Fuzzy Bipartite Consensus for Multiagent Systems: A Novel Scaling Off-Policy Learning Scheme
, Reinforcement-Learning-Based Fuzzy Bipartite Consensus for Multiagent Systems: A Novel Scaling Off-Policy Learning Scheme, IEEE Transactions on Cybernetics, 2025, Cites: 0
Liu J., Shen H., Wang J., Cao J., Rutkowski L., H∞ Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method. (2)
H∞ Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method
, H∞ Control for Interconnected Systems With Unknown System Dynamics: A Two-Stage Reinforcement Learning Method, IEEE Transactions on Automation Science and Engineering, 22, 22, 6388-6397, 2025, Cites: 2
Sevastjanov P., Kaczmarek K., Dymova L., Rutkowski L., Interpretable Forex trading models based on new technical analysis indicators and fuzzy multi-criteria optimization. (0)
Interpretable Forex trading models based on new technical analysis indicators and fuzzy multi-criteria optimization
, Interpretable Forex trading models based on new technical analysis indicators and fuzzy multi-criteria optimization, Fuzzy Sets and Systems, 511, 511, 2025, Cites: 0
Cheng J., Liu N., Rutkowski L., Cao J., Yan H., Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction–Diffusion Neural Networks. (2)
Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction–Diffusion Neural Networks
, Protocol-Based Sampled-Data Control for T-S Fuzzy Reaction–Diffusion Neural Networks, IEEE Transactions on Fuzzy Systems, 33, 33, 1168-1177, 2025, Cites: 22024 (29)
Lu Y., Yao Y., Huang X., Xiao M., Jiang G., Rutkowski L., Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion. (2)
Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion
, Investigation of Spatial Pattern in SI Model with PD Control and Cross-Diffusion, International Journal of Bifurcation and Chaos, 34, 34, 2024, Cites: 2
Huang Z., Lv W., Liu C., Xu Y., Rutkowski L., Huang T., Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks. (11)
Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks
, Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks, IEEE Transactions on Industrial Informatics, 20, 20, 4218-4226, 2024, Cites: 11
Wojtulewicz M., Duda P., Nowicki R., Rutkowski L., On Speeding Up the Training of Deep Neural Networks Using the Streaming Approach: The Base-Values Mechanism. (0)
On Speeding Up the Training of Deep Neural Networks Using the Streaming Approach: The Base-Values Mechanism
, On Speeding Up the Training of Deep Neural Networks Using the Streaming Approach: The Base-Values Mechanism, Proceedings of Machine Learning Research, 263, 263, 17-24, 2024, Cites: 0
Sevastjanov P., Kaczmarek K., Rutkowski L., A multi-model approach to the development of algorithmic trading systems for the Forex market. (7)
A multi-model approach to the development of algorithmic trading systems for the Forex market
, A multi-model approach to the development of algorithmic trading systems for the Forex market, Expert Systems with Applications, 236, 236, 2024, Cites: 7
Rutkowska D., Duda P., Cao J., Jaworski M., Kisiel-Dorohinicki M., Tao D., Rutkowski L., Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring. (4)
Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring
, Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring, Applied Soft Computing, 161, 161, 2024, Cites: 4
Chen G., Xu G., He F., Hong Y., Rutkowski L., Tao D., Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games. (3)
Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games
, Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 46, 10797-10813, 2024, Cites: 3
Li H., Xiao M., Wang Z., Xu F., Wang Z., Zheng W., Rutkowski L., A new chemical networked system: spatial-temporal evolution and control. (0)
A new chemical networked system: spatial-temporal evolution and control
, A new chemical networked system: spatial-temporal evolution and control, Physica Scripta, 99, 99, 2024, Cites: 0
Vovna O., Kaydash H., Rutkowski L., Sakhno I., Laktionov I., Kabanets M., Zozulya S., Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines. (0)
Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines
, Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines, Journal of Control Science and Engineering, 2024, 2024, 2024, Cites: 0
Lv X., Cao J., Rutkowski L., Duan P., Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies. (16)
Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies
, Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies, IEEE Transactions on Automatic Control, 69, 69, 771-782, 2024, Cites: 16
Ju Y., Xiao M., Huang C., Rutkowski L., Cao J., Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus. (3)
Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus
, Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus, International Journal of Systems Science, 55, 55, 2187-2210, 2024, Cites: 3
Wu J., Cheng J., Yan H., Rutkowski L., Cao J., Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems with Stochastic Communication Protocol. (3)
Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems with Stochastic Communication Protocol
, Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems with Stochastic Communication Protocol, IEEE Transactions on Fuzzy Systems, 32, 32, 7109-7117, 2024, Cites: 3
Du X., Xiao M., Luan Y., Ding J., Rutkowski L., Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks. (0)
Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks
, Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks, Journal of Computational and Nonlinear Dynamics, 19, 19, 2024, Cites: 0
Ademola A.T., Wen S., Feng Y., Zhang W., Rutkowski L., Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations. (0)
Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations
, Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations, Proyecciones, 43, 43, 985-1009, 2024, Cites: 0
Zhou Y., Lv W., Tao J., Xu Y., Huang T., Rutkowski L., Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel. (5)
Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel
, Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel, Neural Networks, 169, 169, 485-495, 2024, Cites: 5
He J., Xiao M., He H., Wang Z., Xing Zheng W., Rutkowski L., Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks. (1)
Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks
, Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks, IEEE Transactions on Circuits and Systems I Regular Papers, 71, 71, 4131-4144, 2024, Cites: 1
Lin L., Cao J., Lam J., Rutkowski L., Dimirovski G.M., Zhu S., A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains. (18)
A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains
, A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains, IEEE Transactions on Automatic Control, 69, 69, 5743-5758, 2024, Cites: 18
Duda P., Wojtulewicz M., Rutkowski L., Accelerating deep neural network learning using data stream methodology. (2)
Accelerating deep neural network learning using data stream methodology
, Accelerating deep neural network learning using data stream methodology, Information Sciences, 669, 669, 2024, Cites: 2
Cheng H., Xiao M., Lu Y., Bao H., Rutkowski L., Cao J., Complex pattern evolution of a two-dimensional space diffusion model of malware spread. (2)
Complex pattern evolution of a two-dimensional space diffusion model of malware spread
, Complex pattern evolution of a two-dimensional space diffusion model of malware spread, Physica Scripta, 99, 99, 2024, Cites: 2
Huang X.-X., Xiao M., Rutkowski L., Bao H.-B., Huang X., Cao J.-D., Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks. (1)
Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks
, Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks, Chinese Physics B, 33, 33, 2024, Cites: 1
Kong F., Cao J., Rutkowski L., Zhang Y., Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control. (4)
Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control
, Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control, IEEE Transactions on Fuzzy Systems, 32, 32, 3059-3070, 2024, Cites: 4
Xin Y., Cheng Z., Cao J., Rutkowski L., Wang Y., Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks. (10)
Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks
, Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 35, 35, 1394-1400, 2024, Cites: 10
Lin L., Cao J., Lu J., Rutkowski L., Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy. (10)
Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy
, Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy, IEEE Caa Journal of Automatica Sinica, 11, 11, 806-808, 2024, Cites: 10
Tao M., Guo L., Cao J., Rutkowski L., A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems. (5)
A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems
, A Second-Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems, IEEE Transactions on Circuits and Systems II Express Briefs, 71, 71, 1316-1320, 2024, Cites: 5
Dong S., Tang J., Abbas K., Hou R., Kamruzzaman J., Rutkowski L., Buyya R., Task offloading strategies for mobile edge computing: A survey. (27)
Task offloading strategies for mobile edge computing: A survey
, Task offloading strategies for mobile edge computing: A survey, Computer Networks, 254, 254, 2024, Cites: 27
Lin L., Cao J., Lam J., Zhu S., Azuma S.-I., Rutkowski L., Leader-Follower Consensus Over Finite Fields. (14)
Leader-Follower Consensus Over Finite Fields
, Leader-Follower Consensus Over Finite Fields, IEEE Transactions on Automatic Control, 69, 69, 4718-4725, 2024, Cites: 14
Cheng H., Xiao M., Yu W., Rutkowski L., Cao J., How to regulate pattern formations for malware propagation in cyber-physical systems. (6)
How to regulate pattern formations for malware propagation in cyber-physical systems
, How to regulate pattern formations for malware propagation in cyber-physical systems, Chaos, 34, 34, 2024, Cites: 6
Qi S., Wei W., Wang J., Sun S., Rutkowski L., Huang T., Kacprzyk J., Qi Y., Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage. (6)
Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage
, Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage, IEEE Transactions on Mobile Computing, 23, 23, 2566-2582, 2024, Cites: 6
Urbanczyk A., Kucaba K., Wojtulewicz M., Kisiel-Dorohinicki M., Rutkowski L., Duda P., Kacprzyk J., Yao X., Chong S.Y., Byrski A., (μ +λ) Evolution Strategy with Socio-Cognitive Mutation. (1)
(μ +λ) Evolution Strategy with Socio-Cognitive Mutation
, (μ +λ) Evolution Strategy with Socio-Cognitive Mutation, Journal of Automation Mobile Robotics and Intelligent Systems, 18, 18, 1-11, 2024, Cites: 1
Starzec G., Starzec M., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Ant colony optimization using two-dimensional pheromone for single-objective transport problems. (7)
Ant colony optimization using two-dimensional pheromone for single-objective transport problems
, Ant colony optimization using two-dimensional pheromone for single-objective transport problems, Journal of Computational Science, 79, 79, 2024, Cites: 72023 (27)
Lin A., Cheng J., Rutkowski L., Wen S., Luo M., Cao J., Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol. (25)
Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol
, Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol, IEEE Transactions on Neural Networks and Learning Systems, 34, 34, 9004-9015, 2023, Cites: 25
Laktionov I., Rutkowski L., Vovna O., Byrski A., Kabanets M., A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques. (10)
A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques
, A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques, Engineering Applications of Artificial Intelligence, 126, 126, 2023, Cites: 10
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 13589 LNAI, 13589 LNAI, v-vi, 2023, Cites: 0
Guan H., Liu Y., Kou K.I., Cao J., Rutkowski L., Collaborative neurodynamic optimization for solving nonlinear equations. (7)
Collaborative neurodynamic optimization for solving nonlinear equations
, Collaborative neurodynamic optimization for solving nonlinear equations, Neural Networks, 165, 165, 483-490, 2023, Cites: 7
Ghaffari R., Helfroush M.S., Khosravi A., Kazemi K., Danyali H., Rutkowski L., Toward domain adaptation with open-set target data: Review of theory and computer vision applications. (13)
Toward domain adaptation with open-set target data: Review of theory and computer vision applications
, Toward domain adaptation with open-set target data: Review of theory and computer vision applications, Information Fusion, 100, 100, 2023, Cites: 13
Wang J., Wu J., Shen H., Cao J., Rutkowski L., Fuzzy H∞ Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method. (99)
Fuzzy H∞ Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method
, Fuzzy H∞ Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method, IEEE Transactions on Cybernetics, 53, 53, 7380-7391, 2023, Cites: 99
Urbanczyk A., Kipinski P., Nabywaniec M., Rutkowski L., Chong S.Y., Yao X., Boryczko K., Byrski A., Socio-cognitive caste-based optimization. (0)
Socio-cognitive caste-based optimization
, Socio-cognitive caste-based optimization, Journal of Computational Science, 72, 72, 2023, Cites: 0
Shen H., Zhang Y., Wang J., Cao J., Rutkowski L., Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems. (21)
Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems
, Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems, IEEE Transactions on Automatic Control, 68, 68, 6255-6261, 2023, Cites: 21
Starzec G., Starzec M., Bandyopadhyay S., Maulik U., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Two-Dimensional Pheromone in Ant Colony Optimization. (1)
Two-Dimensional Pheromone in Ant Colony Optimization
, Two-Dimensional Pheromone in Ant Colony Optimization, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 14162 LNAI, 14162 LNAI, 459-471, 2023, Cites: 1
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 13588 LNAI, 13588 LNAI, v-vi, 2023, Cites: 0
Zhu S., Cao J., Lin L., Rutkowski L., Lu J., Lu G., Observability and Detectability of Stochastic Labeled Graphs. (23)
Observability and Detectability of Stochastic Labeled Graphs
, Observability and Detectability of Stochastic Labeled Graphs, IEEE Transactions on Automatic Control, 68, 68, 7299-7311, 2023, Cites: 23
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 14125 LNAI, 14125 LNAI, v-vi, 2023, Cites: 0
Wu X., Zhu X., Baralis E., Lu R., Kumar V., Rutkowski L., Tang J., On Computing Paradigms - Where Will Large Language Models Be Going. (1)
On Computing Paradigms - Where Will Large Language Models Be Going
, On Computing Paradigms - Where Will Large Language Models Be Going, Proceedings IEEE International Conference on Data Mining Icdm, 1577-1582, 2023, Cites: 1
Krokosz T., Rykowski J., Zajecka M., Brzoza-Woch R., Rutkowski L., Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding. (4)
Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding
, Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding, Sensors, 23, 23, 2023, Cites: 4
Duda P., Wojtulewicz M., Rutkowski L., The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach. (0)
The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach
, The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 14125 LNAI, 14125 LNAI, 46-55, 2023, Cites: 0
Zhang N., Wang J., Rutkowski L., Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization. (1)
Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization
, Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization, Neural Computing and Applications, 35, 35, 9947-9949, 2023, Cites: 1
Wang J., Chen Z., Shen H., Cao J., Rutkowski L., Fuzzy H∞ Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation. (8)
Fuzzy H∞ Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation
, Fuzzy H∞ Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation, IEEE Transactions on Fuzzy Systems, 31, 31, 4374-4384, 2023, Cites: 8
Feng Z.C., Xu W.Y., Cao J.D., Yang S.F., Rutkowski L., Distributed online bandit tracking for Nash equilibrium under partial-decision information setting. (0)
Distributed online bandit tracking for Nash equilibrium under partial-decision information setting
, Distributed online bandit tracking for Nash equilibrium under partial-decision information setting, Science China Technological Sciences, 66, 66, 3129-3138, 2023, Cites: 0
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control. (22)
Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control
, Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control, IEEE Transactions on Automatic Control, 68, 68, 1215-1222, 2023, Cites: 22
Zhu W., Cao J., Shi X., Rutkowski L., Leader-following consensus of finite-field networks with time-delays. (3)
Leader-following consensus of finite-field networks with time-delays
, Leader-following consensus of finite-field networks with time-delays, Information Sciences, 647, 647, 2023, Cites: 3
Sevastjanov P., Kaczmarek K., Rutkowski L., A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization. (4)
A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization
, A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization, Applied Soft Computing, 147, 147, 2023, Cites: 4
Wang Y., Yan J., Huang W., Rutkowski L., Cao J., Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track. (7)
Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track
, Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track, Science China Information Sciences, 66, 66, 2023, Cites: 7
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 14126 LNAI, 14126 LNAI, v-vi, 2023, Cites: 0
Izonin I., Tkachenko R., Gurbych O., Kovac M., Rutkowski L., Holoven R., A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis. (7)
A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis
, A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis, Mathematical Biosciences and Engineering, 20, 20, 13398-13414, 2023, Cites: 7
Rutkowska D., Duda P., Cao J., Rutkowski L., Byrski A., Jaworski M., Tao D., The L2 convergence of stream data mining algorithms based on probabilistic neural networks. (7)
The L2 convergence of stream data mining algorithms based on probabilistic neural networks
, The L2 convergence of stream data mining algorithms based on probabilistic neural networks, Information Sciences, 631, 631, 346-368, 2023, Cites: 7
Cheng J., Wu F., Liu L., Zhang Q., Rutkowski L., Tao D., InDecGAN: Learning to Generate Complex Images from Captions via Independent Object-Level Decomposition and Enhancement. (1)
InDecGAN: Learning to Generate Complex Images from Captions via Independent Object-Level Decomposition and Enhancement
, InDecGAN: Learning to Generate Complex Images from Captions via Independent Object-Level Decomposition and Enhancement, IEEE Transactions on Multimedia, 25, 25, 8279-8293, 2023, Cites: 1
Wang J., Wu J., Shen H., Cao J., Rutkowski L., A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism. (18)
A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism
, A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism, IEEE Transactions on Systems Man and Cybernetics Systems, 53, 53, 4934-4943, 2023, Cites: 182022 (16)
Li Z., Tang Y., Fan Y., Huang T., Rutkowski L., Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses. (11)
Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses
, Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses, IEEE Transactions on Network Science and Engineering, 9, 9, 2224-2236, 2022, Cites: 11
Luo Y., Zhu W., Cao J., Rutkowski L., Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems. (45)
Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems
, Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems, IEEE Transactions on Network Science and Engineering, 9, 9, 1527-1539, 2022, Cites: 45
Staszewski P., Jaworski M., Cao J., Rutkowski L., A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers. (24)
A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers
, A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 7913-7920, 2022, Cites: 24
Zhu W., Cao J., Shi X., Rutkowski L., Synchronization of Finite-Field Networks With Time Delays. (13)
Synchronization of Finite-Field Networks With Time Delays
, Synchronization of Finite-Field Networks With Time Delays, IEEE Transactions on Network Science and Engineering, 9, 9, 347-355, 2022, Cites: 13
Grycuk R., Galkowski T., Scherer R., Rutkowski L., A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder. (8)
A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder
, A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder, Proceedings of the International Joint Conference on Neural Networks, 2022-July, 2022-July, 2022, Cites: 8
Tan X., Xiang C., Cao J., Xu W., Wen G., Rutkowski L., Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption. (76)
Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption
, Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption, IEEE Transactions on Cybernetics, 52, 52, 8246-8257, 2022, Cites: 76
Hu J., Cao J., Rutkowski L., Xue C., Yu J., Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators. (12)
Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators
, Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators, Electric Power Systems Research, 208, 208, 2022, Cites: 12
Godzik M., Dajda J., Kisiel-Dorohinicki M., Byrski A., Rutkowski L., Orzechowski P., Wagenaar J., Moore J.H., Applying autonomous hybrid agent-based computing to difficult optimization problems. (1)
Applying autonomous hybrid agent-based computing to difficult optimization problems
, Applying autonomous hybrid agent-based computing to difficult optimization problems, Journal of Computational Science, 64, 64, 2022, Cites: 1
Song Y., Cao J., Rutkowski L., A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy. (70)
A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy
, A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy, IEEE Transactions on Network Science and Engineering, 9, 9, 1154-1162, 2022, Cites: 70
Feng L., Liu L., Cao J., Rutkowski L., Lu G., General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching. (15)
General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching
, General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching, IEEE Transactions on Cybernetics, 52, 52, 5441-5453, 2022, Cites: 15
Zhang N., Wang J., Rutkowski L., Editorial: Special Issue on Reliable Machine Learning and Optimization. (0)
Editorial: Special Issue on Reliable Machine Learning and Optimization
, Editorial: Special Issue on Reliable Machine Learning and Optimization, International Journal on Artificial Intelligence Tools, 31, 31, 2022, Cites: 0
Feng Y., Zhang W., Xiong J., Li H., Rutkowski L., Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes. (17)
Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes
, Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes, IEEE Transactions on Cybernetics, 52, 52, 748-757, 2022, Cites: 17
Yu T., Cao J., Rutkowski L., Luo Y.-P., Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control. (61)
Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control
, Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 3938-3947, 2022, Cites: 61
Li H., Fang J.-A., Li X., Rutkowski L., Huang T., Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays. (27)
Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays
, Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays, IEEE Transactions on Circuits and Systems I Regular Papers, 69, 69, 2095-2107, 2022, Cites: 27
Shen H., Wang X., Wang J., Cao J., Rutkowski L., Robust Composite H∞Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method. (25)
Robust Composite H∞Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method
, Robust Composite H∞Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method, IEEE Transactions on Cybernetics, 52, 52, 12712-12721, 2022, Cites: 25
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control. (19)
Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control
, Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control, IEEE Transactions on Cybernetics, 52, 52, 10290-10301, 2022, Cites: 192021 (13)
Xiong H., Tang Y.Y., Murtagh F., Rutkowski L., Berkovsky S., A diversified shared latent variable model for efficient image characteristics extraction and modelling. (5)
A diversified shared latent variable model for efficient image characteristics extraction and modelling
, A diversified shared latent variable model for efficient image characteristics extraction and modelling, Neurocomputing, 421, 421, 244-259, 2021, Cites: 5
Wang J., Yang C., Shen H., Cao J., Rutkowski L., Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters. (126)
Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters
, Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters, IEEE Transactions on Systems Man and Cybernetics Systems, 51, 51, 7579-7586, 2021, Cites: 126
Duda P., Rutkowski L., Woldan P., Najgebauer P., The Streaming Approach to Training Restricted Boltzmann Machines. (0)
The Streaming Approach to Training Restricted Boltzmann Machines
, The Streaming Approach to Training Restricted Boltzmann Machines, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12854 LNAI, 12854 LNAI, 308-317, 2021, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12855 LNAI, 12855 LNAI, v-vi, 2021, Cites: 0
He J., Liu Y., Lu J., Cao J., Rutkowski L., Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays. (5)
Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays
, Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays, IEEE Transactions on Systems Man and Cybernetics Systems, 51, 51, 6842-6851, 2021, Cites: 5
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12854 LNAI, 12854 LNAI, v-vi, 2021, Cites: 0
Bilski J., Rutkowski L., Smolag J., Tao D., A novel method for speed training acceleration of recurrent neural networks. (23)
A novel method for speed training acceleration of recurrent neural networks
, A novel method for speed training acceleration of recurrent neural networks, Information Sciences, 553, 553, 266-279, 2021, Cites: 23
Jaworski M., Rutkowski L., Staszewski P., Najgebauer P., Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines. (2)
Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines
, Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12854 LNAI, 12854 LNAI, 338-346, 2021, Cites: 2
Lv X., Cao J., Rutkowski L., Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control. (19)
Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control
, Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control, Neural Networks, 143, 143, 515-524, 2021, Cites: 19
Yang X., Wan X., Zunshui C., Cao J., Liu Y., Rutkowski L., Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault. (115)
Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault
, Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault, IEEE Transactions on Neural Networks and Learning Systems, 32, 32, 4191-4201, 2021, Cites: 115
Xu S., Cao J., Liu Q., Rutkowski L., Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method. (18)
Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method
, Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method, IEEE Transactions on Systems Man and Cybernetics Systems, 51, 51, 3617-3628, 2021, Cites: 18
Tan X., Cao J., Rutkowski L., Lu G., Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution. (48)
Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution
, Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution, IEEE Transactions on Cybernetics, 51, 51, 624-634, 2021, Cites: 48
Xia Z., Liu Y., Lu J., Cao J., Rutkowski L., Penalty method for constrained distributed quaternion-variable optimization. (54)
Penalty method for constrained distributed quaternion-variable optimization
, Penalty method for constrained distributed quaternion-variable optimization, IEEE Transactions on Cybernetics, 51, 51, 5631-5636, 2021, Cites: 542020 (32)
Rutkowski L., Jaworski M., Duda P., Splitting Criteria with the Bias Term. (0)
Splitting Criteria with the Bias Term
, Splitting Criteria with the Bias Term, Studies in Big Data, 56, 56, 83-89, 2020, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12416 LNAI, 12416 LNAI, v-vi, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Hybrid Splitting Criteria. (1)
Hybrid Splitting Criteria
, Hybrid Splitting Criteria, Studies in Big Data, 56, 56, 91-113, 2020, Cites: 1
Tan X., Cao J., Rutkowski L., Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay. (82)
Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay
, Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay, IEEE Transactions on Network Science and Engineering, 7, 7, 1111-1120, 2020, Cites: 82
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Data Stream Mining. (18)
Basic Concepts of Data Stream Mining
, Basic Concepts of Data Stream Mining, Studies in Big Data, 56, 56, 13-33, 2020, Cites: 18
Rutkowski L., Jaworski M., Duda P., Introduction and Overview of the Main Results of the Book. (1)
Introduction and Overview of the Main Results of the Book
, Introduction and Overview of the Main Results of the Book, Studies in Big Data, 56, 56, 1-10, 2020, Cites: 1
Lin L., Cao J., Zhu S., Rutkowski L., Lu G., Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model. (36)
Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model
, Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model, IEEE Transactions on Control of Network Systems, 7, 7, 1859-1869, 2020, Cites: 36
Rutkowski L., Jaworski M., Duda P., Splitting Criteria Based on the McDiarmid’s Theorem. (0)
Splitting Criteria Based on the McDiarmid’s Theorem
, Splitting Criteria Based on the McDiarmid’s Theorem, Studies in Big Data, 56, 56, 51-62, 2020, Cites: 0
Cao J.-D., Liu Y., Lu J.-Q., Rutkowski L., Complex systems and networks with their applications. (2)
Complex systems and networks with their applications
, Complex systems and networks with their applications, Frontiers of Information Technology and Electronic Engineering, 21, 21, 195-198, 2020, Cites: 2
Duda P., Rutkowski L., Jaworski M., Rutkowska D., On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification. (32)
On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification
, On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification, IEEE Transactions on Cybernetics, 50, 50, 1683-1696, 2020, Cites: 32
Rutkowski L., Jaworski M., Duda P., The General Procedure of Ensembles Construction in Data Stream Scenarios. (0)
The General Procedure of Ensembles Construction in Data Stream Scenarios
, The General Procedure of Ensembles Construction in Data Stream Scenarios, Studies in Big Data, 56, 56, 281-286, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Probabilistic Neural Networks for the Streaming Data Classification. (3)
Probabilistic Neural Networks for the Streaming Data Classification
, Probabilistic Neural Networks for the Streaming Data Classification, Studies in Big Data, 56, 56, 245-277, 2020, Cites: 3
Rutkowski L., Jaworski M., Duda P., Decision Trees in Data Stream Mining. (9)
Decision Trees in Data Stream Mining
, Decision Trees in Data Stream Mining, Studies in Big Data, 56, 56, 37-50, 2020, Cites: 9
Jaworski M., Rutkowski L., Angelov P., Concept Drift Detection Using Autoencoders in Data Streams Processing. (16)
Concept Drift Detection Using Autoencoders in Data Streams Processing
, Concept Drift Detection Using Autoencoders in Data Streams Processing, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12415 LNAI, 12415 LNAI, 124-133, 2020, Cites: 16
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Probabilistic Neural Networks. (0)
Basic Concepts of Probabilistic Neural Networks
, Basic Concepts of Probabilistic Neural Networks, Studies in Big Data, 56, 56, 117-154, 2020, Cites: 0
Li H., Fang J.-A., Li X., Rutkowski L., Huang T., Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays. (22)
Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays
, Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays, Neural Networks, 132, 132, 447-460, 2020, Cites: 22
Chen B., Cao J., Lu G., Rutkowski L., Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks. (29)
Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks
, Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks, IEEE Transactions on Circuits and Systems II Express Briefs, 67, 67, 2537-2541, 2020, Cites: 29
Lin L., Cao J., Rutkowski L., Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks. (49)
Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks
, Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 1060-1065, 2020, Cites: 49
Staszewski P., Jaworski M., Rutkowski L., Tao D., Explainable Cluster-Based Rules Generation for Image Retrieval and Classification. (1)
Explainable Cluster-Based Rules Generation for Image Retrieval and Classification
, Explainable Cluster-Based Rules Generation for Image Retrieval and Classification, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12416 LNAI, 12416 LNAI, 85-94, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Final Remarks and Challenging Problems. (0)
Final Remarks and Challenging Problems
, Final Remarks and Challenging Problems, Studies in Big Data, 56, 56, 323-327, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Classification. (0)
Classification
, Classification, Studies in Big Data, 56, 56, 287-308, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Misclassification Error Impurity Measure. (2)
Misclassification Error Impurity Measure
, Misclassification Error Impurity Measure, Studies in Big Data, 56, 56, 63-82, 2020, Cites: 2
Liu Y., Zheng Y., Lu J., Cao J., Rutkowski L., Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach. (106)
Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach
, Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 1022-1035, 2020, Cites: 106
Yang X., Liu Y., Cao J., Rutkowski L., Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching. (129)
Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching
, Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 5483-5496, 2020, Cites: 129
Rutkowski L., Jaworski M., Duda P., General Non-parametric Learning Procedure for Tracking Concept Drift. (2)
General Non-parametric Learning Procedure for Tracking Concept Drift
, General Non-parametric Learning Procedure for Tracking Concept Drift, Studies in Big Data, 56, 56, 155-172, 2020, Cites: 2
Wang Z., Cao J., Cai Z., Rutkowski L., Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay. (77)
Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay
, Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay, IEEE Transactions on Cybernetics, 50, 50, 2758-2769, 2020, Cites: 77
Rutkowski T., Nielek R., Rutkowska D., Rutkowski L., A Novel Explainable Recommender for Investment Managers. (2)
A Novel Explainable Recommender for Investment Managers
, A Novel Explainable Recommender for Investment Managers, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12416 LNAI, 12416 LNAI, 412-422, 2020, Cites: 2
Rutkowski L., Jaworski M., Duda P., Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks. (1)
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks
, Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks, Studies in Big Data, 56, 56, 173-244, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Regression. (0)
Regression
, Regression, Studies in Big Data, 56, 56, 309-322, 2020, Cites: 0
Chen B., Cao J., Luo Y., Rutkowski L., Asymptotic Output Tracking of Probabilistic Boolean Control Networks. (43)
Asymptotic Output Tracking of Probabilistic Boolean Control Networks
, Asymptotic Output Tracking of Probabilistic Boolean Control Networks, IEEE Transactions on Circuits and Systems I Regular Papers, 67, 67, 2780-2790, 2020, Cites: 43
Najgebauer P., Scherer R., Rutkowski L., Fully Convolutional Network for Removing DCT Artefacts from Images. (8)
Fully Convolutional Network for Removing DCT Artefacts from Images
, Fully Convolutional Network for Removing DCT Artefacts from Images, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 8
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12415 LNAI, 12415 LNAI, v-vi, 2020, Cites: 02019 (9)
Rutkowska D., Rutkowski L., On the hermite series-based generalized regression neural networks for stream data mining. (1)
On the hermite series-based generalized regression neural networks for stream data mining
, On the hermite series-based generalized regression neural networks for stream data mining, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11955 LNCS, 11955 LNCS, 437-448, 2019, Cites: 1
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)). (0)
Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028))
, Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)), Information Sciences, 477, 477, 545, 2019, Cites: 0
Najgebauer P., Grycuk R., Rutkowski L., Scherer R., Siwocha A., Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection. (6)
Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection
, Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11508 LNAI, 11508 LNAI, 164-171, 2019, Cites: 6
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11508 LNAI, 11508 LNAI, v-vi, 2019, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11509 LNAI, 11509 LNAI, v-vi, 2019, Cites: 0
Feng L., Cao J., Hu J., Wu Z., Rutkowski L., Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode. (3)
Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode
, Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode, Neural Processing Letters, 50, 50, 2797-2819, 2019, Cites: 3
Jaworski M., Duda P., Rutkowska D., Rutkowski L., On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine. (2)
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine
, On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine, Communications in Computer and Information Science, 1143 CCIS, 1143 CCIS, 347-354, 2019, Cites: 2
Woldan P., Staszewski P., Rutkowski L., Grzanek K., On Proper Designing of Deep Structures for Image Classification. (0)
On Proper Designing of Deep Structures for Image Classification
, On Proper Designing of Deep Structures for Image Classification, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11508 LNAI, 11508 LNAI, 223-235, 2019, Cites: 0
Jaworski M., Rutkowski L., Duda P., Cader A., Resource-aware data stream mining using the restricted boltzmann machine. (4)
Resource-aware data stream mining using the restricted boltzmann machine
, Resource-aware data stream mining using the restricted boltzmann machine, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11509 LNAI, 11509 LNAI, 384-396, 2019, Cites: 42018 (11)
Duda P., Jaworski M., Rutkowski L., Online grnn-based ensembles for regression on evolving data streams. (6)
Online grnn-based ensembles for regression on evolving data streams
, Online grnn-based ensembles for regression on evolving data streams, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10878 LNCS, 10878 LNCS, 221-228, 2018, Cites: 6
Duda P., Jaworski M., Rutkowski L., Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks. (31)
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks
, Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks, International Journal of Neural Systems, 28, 28, 2018, Cites: 31
Jaworski M., Duda P., Rutkowski L., Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine. (14)
Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine
, Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine, Proceedings of the International Joint Conference on Neural Networks, 2018-July, 2018-July, 2018, Cites: 14
Jaworski M., Duda P., Rutkowski L., New Splitting Criteria for Decision Trees in Stationary Data Streams. (90)
New Splitting Criteria for Decision Trees in Stationary Data Streams
, New Splitting Criteria for Decision Trees in Stationary Data Streams, IEEE Transactions on Neural Networks and Learning Systems, 29, 29, 2516-2529, 2018, Cites: 90
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10842 LNAI, 10842 LNAI, V-VI, 2018, Cites: 0
Rutkowski T., Romanowski J., Woldan P., Staszewski P., Nielek R., Rutkowski L., A content-based recommendation system using neuro-fuzzy approach. (50)
A content-based recommendation system using neuro-fuzzy approach
, A content-based recommendation system using neuro-fuzzy approach, IEEE International Conference on Fuzzy Systems, 2018-July, 2018-July, 2018, Cites: 50
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10841 LNAI, 10841 LNAI, V-VI, 2018, Cites: 0
Lapa K., Cpalka K., Rutkowski L., New aspects of interpretability of fuzzy systems for nonlinear modeling. (16)
New aspects of interpretability of fuzzy systems for nonlinear modeling
, New aspects of interpretability of fuzzy systems for nonlinear modeling, Studies in Computational Intelligence, 738, 738, 225-264, 2018, Cites: 16
Cao J., Rutkowski L., On the global convergence of the parzen-based generalized regression neural networks applied to streaming data. (1)
On the global convergence of the parzen-based generalized regression neural networks applied to streaming data
, On the global convergence of the parzen-based generalized regression neural networks applied to streaming data, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10841 LNAI, 10841 LNAI, 25-34, 2018, Cites: 1
Duda P., Jaworski M., Rutkowski L., Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks. (25)
Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks
, Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences, 460-461, 460-461, 497-518, 2018, Cites: 25
Zalasinski M., Cpalka K., Rutkowski L., Fuzzy-genetic approach to identity verification using a handwritten signature. (6)
Fuzzy-genetic approach to identity verification using a handwritten signature
, Fuzzy-genetic approach to identity verification using a handwritten signature, Studies in Computational Intelligence, 738, 738, 375-394, 2018, Cites: 62017 (8)
Najgebauer P., Rutkowski L., Scherer R., Interest point localization based on edge detection according to gestalt laws. (2)
Interest point localization based on edge detection according to gestalt laws
, Interest point localization based on edge detection according to gestalt laws, 2017 2nd IEEE International Conference on Computational Intelligence and Applications Iccia 2017, 2017-January, 2017-January, 349-353, 2017, Cites: 2
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., How to adjust an ensemble size in stream data mining?. (67)
How to adjust an ensemble size in stream data mining?
, How to adjust an ensemble size in stream data mining?, Information Sciences, 381, 381, 46-54, 2017, Cites: 67
Jaworski M., Duda P., Rutkowski L., Najgebauer P., Pawlak M., Heuristic regression function estimation methods for data streams with concept drift. (10)
Heuristic regression function estimation methods for data streams with concept drift
, Heuristic regression function estimation methods for data streams with concept drift, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10246 LNAI, 10246 LNAI, 726-737, 2017, Cites: 10
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10246 LNAI, 10246 LNAI, V-VI, 2017, Cites: 0
Duda P., Jaworski M., Rutkowski L., On ensemble components selection in data streams scenario with reoccurring concept-drift. (16)
On ensemble components selection in data streams scenario with reoccurring concept-drift
, On ensemble components selection in data streams scenario with reoccurring concept-drift, 2017 IEEE Symposium Series on Computational Intelligence Ssci 2017 Proceedings, 2018-January, 2018-January, 1-7, 2017, Cites: 16
Jaworski M., Duda P., Rutkowski L., On applying the Restricted Boltzmann Machine to active concept drift detection. (22)
On applying the Restricted Boltzmann Machine to active concept drift detection
, On applying the Restricted Boltzmann Machine to active concept drift detection, 2017 IEEE Symposium Series on Computational Intelligence Ssci 2017 Proceedings, 2018-January, 2018-January, 1-8, 2017, Cites: 22
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 10245 LNAI, 10245 LNAI, v-vi, 2017, Cites: 0
Najgebauer P., Rutkowski L., Scherer R., Novel method for joining missing line fragments for medical image analysis. (3)
Novel method for joining missing line fragments for medical image analysis
, Novel method for joining missing line fragments for medical image analysis, 2017 22nd International Conference on Methods and Models in Automation and Robotics Mmar 2017, 861-866, 2017, Cites: 32016 (8)
Korytkowski M., Rutkowski L., Scherer R., Fast image classification by boosting fuzzy classifiers. (142)
Fast image classification by boosting fuzzy classifiers
, Fast image classification by boosting fuzzy classifiers, Information Sciences, 327, 327, 175-182, 2016, Cites: 142
Jaworski M., Rutkowski L., Pawlak M., Hybrid splitting criterion in decision trees for data stream mining. (7)
Hybrid splitting criterion in decision trees for data stream mining
, Hybrid splitting criterion in decision trees for data stream mining, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 9693, 9693, 60-72, 2016, Cites: 7
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 9692, 9692, V-VI, 2016, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 9693, 9693, V-VI, 2016, Cites: 0
Nowicki R.K., Scherer R., Rutkowski L., Novel rough neural network for classification with missing data. (5)
Novel rough neural network for classification with missing data
, Novel rough neural network for classification with missing data, 2016 21st International Conference on Methods and Models in Automation and Robotics Mmar 2016, 820-825, 2016, Cites: 5
Cpalka K., Zalasinski M., Rutkowski L., A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. (92)
A new algorithm for identity verification based on the analysis of a handwritten dynamic signature
, A new algorithm for identity verification based on the analysis of a handwritten dynamic signature, Applied Soft Computing Journal, 43, 43, 47-56, 2016, Cites: 92
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., A method for automatic adjustment of ensemble size in stream data mining. (18)
A method for automatic adjustment of ensemble size in stream data mining
, A method for automatic adjustment of ensemble size in stream data mining, Proceedings of the International Joint Conference on Neural Networks, 2016-October, 2016-October, 9-15, 2016, Cites: 18
Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M., Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I. (0)
Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I
, Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 9692, 9692, 2016, Cites: 02015 (5)
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., A new method for data stream mining based on the misclassification error. (105)
A new method for data stream mining based on the misclassification error
, A new method for data stream mining based on the misclassification error, IEEE Transactions on Neural Networks and Learning Systems, 26, 26, 1048-1059, 2015, Cites: 105
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 9119, 9119, V-VII, 2015, Cites: 0
Najgebauer P., Rygal J., Nowak T., Romanowski J., Rutkowski L., Voloshynovskiy S., Scherer R., Fast dictionary matching for content-based image retrieval. (2)
Fast dictionary matching for content-based image retrieval
, Fast dictionary matching for content-based image retrieval, Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 9119, 9119, 747-756, 2015, Cites: 2
Szarek A., Korytkowski M., Rutkowski L., Scherer M., Szyprowski J., Kostadinov D., Customization of joint articulations using soft computing methods. (1)
Customization of joint articulations using soft computing methods
, Customization of joint articulations using soft computing methods, Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 9120, 9120, 151-160, 2015, Cites: 1
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 9120, 9120, V-VII, 2015, Cites: 02014 (6)
Duda P., Jaworski M., Pietruczuk L., Rutkowski L., A novel application of Hoeffding's inequality to decision trees construction for data streams. (17)
A novel application of Hoeffding's inequality to decision trees construction for data streams
, A novel application of Hoeffding's inequality to decision trees construction for data streams, Proceedings of the International Joint Conference on Neural Networks, 3324-3330, 2014, Cites: 17
Cpalka K., Zalasinski M., Rutkowski L., New method for the on-line signature verification based on horizontal partitioning. (88)
New method for the on-line signature verification based on horizontal partitioning
, New method for the on-line signature verification based on horizontal partitioning, Pattern Recognition, 47, 47, 2652-2661, 2014, Cites: 88
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., Decision trees for mining data streams based on the gaussian approximation. (144)
Decision trees for mining data streams based on the gaussian approximation
, Decision trees for mining data streams based on the gaussian approximation, IEEE Transactions on Knowledge and Data Engineering, 26, 26, 108-119, 2014, Cites: 144
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., The Parzen kernel approach to learning in non-stationary environment. (12)
The Parzen kernel approach to learning in non-stationary environment
, The Parzen kernel approach to learning in non-stationary environment, Proceedings of the International Joint Conference on Neural Networks, 3319-3323, 2014, Cites: 12
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (1)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 8467 LNAI, 8467 LNAI, VI-VIII, 2014, Cites: 1
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., The CART decision tree for mining data streams. (280)
The CART decision tree for mining data streams
, The CART decision tree for mining data streams, Information Sciences, 266, 266, 1-15, 2014, Cites: 2802013 (3)
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On design of flexible neuro-fuzzy systems for nonlinear modelling. (68)
On design of flexible neuro-fuzzy systems for nonlinear modelling
, On design of flexible neuro-fuzzy systems for nonlinear modelling, International Journal of General Systems, 42, 42, 706-720, 2013, Cites: 68
Gabryel M., Korytkowski M., Scherer R., Rutkowski L., Object detection by simple fuzzy classifiers generated by boosting. (24)
Object detection by simple fuzzy classifiers generated by boosting
, Object detection by simple fuzzy classifiers generated by boosting, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 7894 LNAI, 7894 LNAI, 540-547, 2013, Cites: 24
Rutkowski L., Pietruczuk L., Duda P., Jaworski M., Decision trees for mining data streams based on the mcdiarmid's bound. (170)
Decision trees for mining data streams based on the mcdiarmid's bound
, Decision trees for mining data streams based on the mcdiarmid's bound, IEEE Transactions on Knowledge and Data Engineering, 25, 25, 1272-1279, 2013, Cites: 1702012 (6)
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (0)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 7269 LNCS, 7269 LNCS, 2012, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 7267 LNAI, 7267 LNAI, V-VI, 2012, Cites: 0
Rutkowski L., Cpalka K., Nowicki R., Pokropinska A., Scherer R., Neuro-fuzzy systems. (2)
Neuro-fuzzy systems
, Neuro-fuzzy systems, Computational Complexity Theory Techniques and Applications, 9781461418009, 9781461418009, 2069-2081, 2012, Cites: 2
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Application of neural networks in assessing changes around implant after total hip arthroplasty. (23)
Application of neural networks in assessing changes around implant after total hip arthroplasty
, Application of neural networks in assessing changes around implant after total hip arthroplasty, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 7268 LNAI, 7268 LNAI, 335-340, 2012, Cites: 23
Rutkowski L., Przybyl A., Cpalka K., Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. (91)
Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation
, Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation, IEEE Transactions on Industrial Electronics, 59, 59, 1238-1247, 2012, Cites: 91
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Forecasting wear of head and acetabulum in hip joint implant. (14)
Forecasting wear of head and acetabulum in hip joint implant
, Forecasting wear of head and acetabulum in hip joint implant, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 7268 LNAI, 7268 LNAI, 341-346, 2012, Cites: 142011 (4)
Rutkowski L., Foreword. (0)
Foreword
, Foreword, Intelligent Systems Reference Library, 6, 6, VII-VIII, 2011, Cites: 0
Korytkowski M., Rutkowski L., Scherer R., Rule base normalization in Takagi-Sugeno ensemble. (3)
Rule base normalization in Takagi-Sugeno ensemble
, Rule base normalization in Takagi-Sugeno ensemble, IEEE Ssci 2011 Symposium Series on Computational Intelligence Hima 2011 2011 IEEE Workshop on Hybrid Intelligent Models and Applications, 1-5, 2011, Cites: 3
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On designing of flexible neuro-fuzzy systems for nonlinear modelling. (2)
On designing of flexible neuro-fuzzy systems for nonlinear modelling
, On designing of flexible neuro-fuzzy systems for nonlinear modelling, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6743 LNAI, 6743 LNAI, 147-154, 2011, Cites: 2
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., AdaBoost ensemble of DCOG rough-neuro-fuzzy systems. (25)
AdaBoost ensemble of DCOG rough-neuro-fuzzy systems
, AdaBoost ensemble of DCOG rough-neuro-fuzzy systems, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6922 LNAI, 6922 LNAI, 62-71, 2011, Cites: 252010 (10)
Du J., Er M.J., Rutkowski L., An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting. (3)
An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting
, An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6113 LNAI, 6113 LNAI, 49-57, 2010, Cites: 3
Rutkowski L., Przybyl A., Cpalka K., Er M.J., Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. (54)
Online speed profile generation for industrial machine tool based on neuro-fuzzy approach
, Online speed profile generation for industrial machine tool based on neuro-fuzzy approach, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6114 LNAI, 6114 LNAI, 645-650, 2010, Cites: 54
Gabryel M., Rutkowski L., Evolutionary designing of logic-type fuzzy systems. (5)
Evolutionary designing of logic-type fuzzy systems
, Evolutionary designing of logic-type fuzzy systems, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6114 LNAI, 6114 LNAI, 143-148, 2010, Cites: 5
Li X., Er M.J., Lim B.S., Zhou J.H., Gan O.P., Rutkowski L., Fuzzy regression modeling for tool performance prediction and degradation detection. (68)
Fuzzy regression modeling for tool performance prediction and degradation detection
, Fuzzy regression modeling for tool performance prediction and degradation detection, International Journal of Neural Systems, 20, 20, 405-419, 2010, Cites: 68
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (5)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6114 LNAI, 6114 LNAI, V-VI, 2010, Cites: 5
Cpalka K., Rutkowski L., Er M.J., On automatic design of neuro-fuzzy systems. (0)
On automatic design of neuro-fuzzy systems
, On automatic design of neuro-fuzzy systems, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6113 LNAI, 6113 LNAI, 43-48, 2010, Cites: 0
Korytkowski M., Rutkowski L., Scherer R., Szarek A., Neural network-based assessment of femur stress after hip joint alloplasty. (0)
Neural network-based assessment of femur stress after hip joint alloplasty
, Neural network-based assessment of femur stress after hip joint alloplasty, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6113 LNAI, 6113 LNAI, 621-626, 2010, Cites: 0
Liu F., Er M.J., Rutkowski L., An online approach towards self-generating fuzzy neural networks with applications. (0)
An online approach towards self-generating fuzzy neural networks with applications
, An online approach towards self-generating fuzzy neural networks with applications, Proceedings of the International Joint Conference on Neural Networks, 2010, Cites: 0
Du J., Er M.J., Rutkowski L., Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach. (6)
Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach
, Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach, Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 6113 LNAI, 6113 LNAI, 58-65, 2010, Cites: 6
Korytkowski M., Nowicki R.K., Scherer R., Rutkowski L., MICOG defuzzification rough-neuro-fuzzy system ensemble. (6)