Instytut Inteligentnych
Systemów Informatycznych

Rok: 2012

• M. Jaworski i Y. Hayashi, "On the Application of the Parzen-Type Kernel Probabilistic Neural Network and Recursive Least Squares Method for Learning in a Time-Varying Environment", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 490-500. Więcej [Link]
• M. Jaworski i M. Er, "Learning in a Non-stationary Environment Using the Recursive Least Squares Method and Orthogonal-Series Type Regression Neural Network", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 480-489. Więcej [Link]
• P. Duda i J. Zurada, "On the Cesaro Orthogonal Series-Type Kernel Probabilistic Neural Networks Handling Non-stationary Noise", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 435-442. Więcej [Link]
• J. Zurada i M. Jaworski, "Learning in a Time-Varying Environment by Making Use of the Stochastic Approximation and Orthogonal Series-Type Kernel Probabilistic Neural Network", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 539-548. Więcej [Link]
• M. Er i P. Duda, "On the Weak Convergence of the Orthogonal Series-Type Kernel Regresion Neural Networks in a Non-stationary Environment", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 443-450. Więcej [Link]
• L. Pietruczuk i J. Zurada, "Weak Convergence of the Recursive Parzen-Type Probabilistic Neural Network in a Non-stationary Environment", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 521-529. Więcej [Link]
• L. Pietruczuk i M. Er, "Strong Convergence of the Parzen-Type Probabilistic Neural Network in a Time-Varying Environment", Parallel Processing and Applied Mathematics, Wyrzykowski et al (Eds.), Springer Berlin Heidelberg, 2012, pp. 530-538. Więcej [Link]
• Y. Hayashi i L. Pietruczuk. "On General Regression Neural Network in a Nonstationary Environment", vol. 7203. 2012, pp. 461-469. Więcej [Link]

Rok: 2011

• M. Jaworski, L. Pietruczuk, P. Duda i L. Rutkowski. "On optimization of resources in stream data mining system". Selected Topics in Computer Science Applications. D. Rutkowska, A. Cader i K. Przybyszewski (Eds.). 2011. Więcej
• P. Duda, L. Pietruczuk, L. Rutkowski i M. Jaworski. "On selection of data pre-processing procedures in stream data mining systems". Selected Topics in Computer Science Applications. D. Rutkowska, A. Cader i K. Przybyszewski (Eds.). 2011. Więcej
• M. Jaworski, L. Pietruczuk, P. Duda i L. Rutkowski. "Fuzzy clustering for data streams". Selected Topics in Computer Science Applications. D. Rutkowska, A. Cader i K. Przybyszewski (Eds.). 2011. Więcej
• L. Pietruczuk, L. Rutkowski, P. Duda i M. Jaworski. "Ensemble based classification in data streams". Selected Topics in Computer Science Applications. D. Rutkowska, A. Cader i K. Przybyszewski (Eds.). 2011. Więcej
• M. Zalasiński i K. Cpałka, "A new method of on-line signature verification using a flexible fuzzy one-class classifier", Selected Topics in Computer Science Applications, Academic Publishing House EXIT, 2011, pp. 38-53. Więcej
• M. Scherer i K. Cpałka, "Neuro-fuzzy-based approach to decision making support", Selected Topics in Computer Science Applications, Academic Publishing House EXIT, 2011, pp. 200-210. Więcej
• K. Cpałka, M. Orzyłowski i L. Rutkowski. "Neuro-fuzzy structures and their applications", Some Aspects of Computer Science, Academic Publishing House EXIT, 2011, pp. 122-132. Więcej
• K. Cpałka. "New methods for designing and reduction of neuro-fuzzy systems", Journal of Applied Computer Science Methods, vol. 2. 2011, pp. 113-126. Więcej
• R. Nowicki, J. Starczewski i M. Orzyłowski. "On non-singleton fuzzification in some neuro-fuzzy architectures", Some Aspects of Computer Science, D. Rutkowska, J. Kacprzyk, A. Cader i K. Przybyszewski (Eds.), EXIT Academic Publishing House, Warsaw 2011, pp. 133-146. Więcej
• K. Cpałka, O. Rebrova, R. Nowicki i L. Rutkowski. "On designing of flexible neuro-fuzzy systems for nonlinear modeling", Lecture Notes in Artificial Intelligence, vol. 6743. 2011, pp. 147-154. Więcej
• M. Korytkowski, R. Nowicki, L. Rutkowski i R. Scherer, "AdaBoost Ensemble of DCOG Rough–Neuro–Fuzzy Systems", Computational Collective Intelligence. Technologies and Applications, Jedrzejowicz, Piotr, Nguyen, Ngoc, Hoang i Kiem (Eds.), Springer Berlin / Heidelberg, 2011, pp. 62-71. Więcej [Link]
• M. Korytkowski, R. Nowicki, L. Rutkowski i R. Scherer. "AdaBoost Ensemble of DCOG Rough–Neuro–Fuzzy Systems", Lectures Notes in Computer Systems, vol. 6922. 2011, pp. 62-71. Więcej
• M. Korytkowski, L. Rutkowski i R. Scherer. "Rule Base Normalization in Takagi-Sugeno Ensemble". IEEE Symposium Series on Computational Intelligence - SSCI 2011, April 11-15, 2011 - Paris, France, 2011 IEEE Workshop on Hybrid Intelligent Models and Applications. 2011. pp. 1-5. Więcej
• R. Scherer. "An Ensemble of Logical-type Neuro-fuzzy Systems", Expert Systems With Applications. 2011. Więcej

Rok: 2010

• L. Rutkowski, A. Przybył, K. Cpałka i E. M. Joo. "Online Speed Profile Generation for Industrial Machine Tool Based on Neuro Fuzzy Approach", Lecture Notes in Artificial Intelligence, vol. 6113. 2010, pp. 645-650. Więcej
• K. Cpałka, L. Rutkowski i E. M. Joo. "On Automatic Design of Neuro Fuzzy Systems", Lecture Notes in Artificial Intelligence, vol. 6113. 2010, pp. 42-48. Więcej
• M. Gabryel, M. Korytkowski, R. Scherer, A. Pokropinska i S. Drozda. "Evolutionary Learning for Neuro-fuzzy Ensembles with Generalized Parametric Triangular Norms", Lecture Notes in Artificial Intelligence, vol. 6113. 2010, pp. 74-79. Więcej [Link]