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Published in:
2015 | OriginalPaper | Chapter
A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification
Authors : Krystian Łapa, Krzysztof Cpałka, Alexander I. Galushkin
Published in: Artificial Intelligence and Soft Computing
Publisher: Springer International Publishing
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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.