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Non-singleton Interval Type-2 Fuzzy Systems as Integration Methods in Modular Neural Networks Used Genetic Algorithms to Design

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Nature-Inspired Design of Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 667))

Abstract

In this paper, we propose the use of Non-Singleton Interval Type-2 Fuzzy Systems (NSIT2FI) automatically designed through genetic algorithms as integration method of modular neural networks (MNN’s) for multimodal biometrics. The goal is to obtain such fuzzy systems as integrators, better recognition rate, and best mean square error in MNN. The results shown comparison between interval type-2 fuzzy systems and Non-singleton Type-2 Fuzzy Systems, where we can observe showing a significant difference that we can get higher recognition rate using non-singleton type-2 fuzzy logic.

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Author information

Authors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Denisse Hidalgo & Patricia Melin

  2. School of Engineering, UABC University, Tijuana, Mexico

    Juan R. Castro

Authors
  1. Denisse Hidalgo
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  2. Patricia Melin
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  3. Juan R. Castro
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Corresponding author

Correspondence to Denisse Hidalgo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Hidalgo, D., Melin, P., Castro, J.R. (2017). Non-singleton Interval Type-2 Fuzzy Systems as Integration Methods in Modular Neural Networks Used Genetic Algorithms to Design. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_56

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  • DOI: https://doi.org/10.1007/978-3-319-47054-2_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47053-5

  • Online ISBN: 978-3-319-47054-2

  • eBook Packages: EngineeringEngineering (R0)

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