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Recent Developments and New Direction in Soft-Computing Foundations and Applications pp 127–139Cite as

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

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 342))

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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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Daniela Sánchez, Patricia Melin & Oscar Castillo

Authors
  1. Daniela Sánchez
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  2. Patricia Melin
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  3. Oscar Castillo
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Corresponding author

Correspondence to Daniela Sánchez .

Editor information

Editors and Affiliations

  1. University of California, Berkeley, USA

    Lotfi A. Zadeh

  2. Tech. of the Republic of Azerbaijan, Ministry of Communications Info., Baku, Azerbaijan

    Ali M. Abbasov

  3. Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  4. Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  5. University of Alberta, Edmonton, Alberta, Canada

    Marek Z. Reformat

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© 2016 Springer International Publishing Switzerland

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Sánchez, D., Melin, P., Castillo, O. (2016). Optimization of Type-1 and Type-2 Fuzzy Systems Applied to Pattern Recognition. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-32229-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32227-8

  • Online ISBN: 978-3-319-32229-2

  • eBook Packages: EngineeringEngineering (R0)

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