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2016 | OriginalPaper | Buchkapitel

Optimization of Type-1 and Type-2 Fuzzy Systems Applied to Pattern Recognition

verfasst von : Daniela Sánchez, Patricia Melin, Oscar Castillo

Erschienen in: Recent Developments and New Direction in Soft-Computing Foundations and Applications

Verlag: Springer International Publishing

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Abstract

In this paper, a new method of fuzzy inference system optimization using a hierarchical genetic algorithm (HGA) is proposed. The fuzzy inference system is used to combine the different responses of modular neural networks (MMNs). In this case, the MMNs are used to perform the human recognition using 4 biometric measures: face, iris, ear, and voice. The main idea is the optimization of some parameters of a fuzzy inference system such as the type of fuzzy logic (FL), type of system, number of membership functions in each input, type of membership functions in each variable, their parameters, and the consequences of the fuzzy rules.

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Titel: Optimization of Type-1 and Type-2 Fuzzy Systems Applied to Pattern Recognition
verfasst von: Daniela Sánchez
Patricia Melin
Oscar Castillo
Verlag: Springer International Publishing
Buch: Recent Developments and New Direction in Soft-Computing Foundations and Applications
Print ISBN: 978-3-319-32227-8

Electronic ISBN: 978-3-319-32229-2

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-32229-2_10

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