Evolutionary learning for neuro-fuzzy ensembles with generalized parametric triangular norms

Authors:
Marcin Gabryel;Marcin Korytkowski;Agata Pokropinska;Rafał Scherer;Stanisław Drozda
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Kotarbinski Olsztyn Academy of Computer Science and Management, Olsztyn, Poland;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Kotarbinski Olsztyn Academy of Computer Science and Management, Olsztyn, Poland;Institute of Mathematics and Computer Science, Jan Długosz University, Czestochowa, Poland;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Academy of Management, SWSPiZ, Institute of Information Technology, Łódź, Poland;University of Warmia and Mazury in Olsztyn, The Faculty of Mathematics and Computer Sciences, Olsztyn, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

In this paper we present a method for designing neurofuzzy systems with Mamdani-type inference and parametric t-norm connecting rule antecedents. Hamacher product was used as t-norm. The neurofuzzy systems are used to create an ensemble of classifiers. After obtaining the ensemble by bagging, every neuro-fuzzy system has its t-norm parameters fine-tuned. Thanks to this the accuracy is improved and the number of parameters can be reduced. The proposed method is tested on a well known benchmark.