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Comparison of Type-2 Fuzzy Integration for Optimized Modular Neural Networks Applied to Human Recognition

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Complex Systems: Solutions and Challenges in Economics, Management and Engineering

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 125))

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Abstract

In this paper optimization techniques for Modular Neural Networks (MNNs) and their combination with a granular approach is presented. A Firefly Algorithm (FA) and a Grey Wolf Optimizer (GWO) are developed to perform modular neural networks (MNN) optimization. These algorithms perform the optimization of some parameters of MNN such as; number of sub modules, percentage of information for the training phase and number of hidden layers (with their respective number of neurons) for each sub module and learning algorithm. The modular neural networks are applied to human recognition based on face, iris, ear and voice. The minimization of the error of recognition is the objective function. To combine the responses of the modular neural networks different type-2 fuzzy inference system are proposed and a comparison of results is performed.

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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. Lyon Neurosciences Research Center and Biomechanical and Impacts Laboratory, University Lyon 1, Villeurbanne, France

    Christian Berger-Vachon

  2. Department of Economics and Business Organization, University of Barcelona, Barcelona, Spain

    Anna María Gil Lafuente

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

    Janusz Kacprzyk

  4. Department of Intelligent Information Systems, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine

    Yuriy Kondratenko

  5. Department of Management Control and Information Systems, University of Chile, Santiago, Chile

    José M. Merigó

  6. Department of Civil, Energy, Environment and Materials Engineering, Mediterranean University of Reggio Calabria, Reggio Calabria, Italy

    Carlo Francesco Morabito

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Sánchez, D., Melin, P., Castillo, O. (2018). Comparison of Type-2 Fuzzy Integration for Optimized Modular Neural Networks Applied to Human Recognition. In: Berger-Vachon, C., Gil Lafuente, A., Kacprzyk, J., Kondratenko, Y., Merigó, J., Morabito, C. (eds) Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Studies in Systems, Decision and Control, vol 125. Springer, Cham. https://doi.org/10.1007/978-3-319-69989-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-69989-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69988-2

  • Online ISBN: 978-3-319-69989-9

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