Polish (Poland)English (United Kingdom)

The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features

Type of Publication: In Proceedings Keywords: approximation theory;fuzzy neural nets;learning (artificial intelligence);rough set theory;approximation system;fuzzy rough sets;gradient learning method;missing features;neuro-fuzzy approximator learning;neurofuzzy system architecture;Approximation
Year: 2014
  • R. K. Nowicki
  • B. A. Nowak
  • J. T. Starczewski
  • K. Cpalka
Book title: Neural Networks (IJCNN), 2014 International Joint Conference on
Pages: 3759-3766
Month: July
The architecture of neuro-fuzzy systems with fuzzy rough sets originally has been developed to process with imprecise data. In this paper, the adaptation of those systems to the missing features case is presented. However, the main considerations concern with methods of learning which could be applied to such systems for approximation tasks. Various methods for determining values of system parameters have been considered, in particular the gradient learning method. The effectiveness of proposed methods has been confirmed by many simulation experiments, which results have been supplied to this paper.

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