A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Autor:	Krzysztof Cpałka, Alexander I. Galushkin, Krystian Łapa
Rok vydání:	2015
Předmět:	ComputingMethodologies_PATTERNRECOGNITION Fuzzy rule Neuro-fuzzy Computer science business.industry Fuzzy set Evolutionary algorithm Particle swarm optimization Imperialist competitive algorithm Fuzzy control system Artificial intelligence business Interpretability
Zdroj:	Artificial Intelligence and Soft Computing ISBN: 9783319193236 ICAISC (1)
DOI:	10.1007/978-3-319-19324-3_41
Popis:	In this paper a new approach for construction of neuro-fuzzy systems for nonlinear classification is introduced. In particular, we concentrate on the flexible neuro-fuzzy systems which allow us to extend notation of rules with weights of fuzzy sets. The proposed approach uses possibilities of hybrid evolutionary algorithm and interpretability criteria of expert knowledge. These criteria include not only complexity of the system, but also semantics of the rules. The approach presented in our paper was tested on typical nonlinear classification simulation problems.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e3811266aec9c9861cbe5feb8d402ad3 https://doi.org/10.1007/978-3-319-19324-3_41 Zobrazit plný text záznamu