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Nature-Inspired Design of Hybrid Intelligent Systems pp 225–241Cite as

Comparison of Optimization Techniques for Modular Neural Networks Applied to Human Recognition

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

Abstract

In this paper a comparison of optimization techniques for a Modular Neural Network (MNN) with a granular approach is presented. A Hierarchical Genetic Algorithm, a Firefly Algorithm (FA), and a Grey Wolf Optimizer are developed to perform a comparison of results. These algorithms design optimal MNN architectures, where their main task is 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 MNNs are applied to human recognition based on iris biometrics, where a benchmark database is used to perform the comparison, having as objective function in each optimization algorithm the minimization of the error of recognition.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Daniela Sánchez & Patricia Melin

  2. León Institute of Technology, León, Mexico

    Juan Carpio & Hector Puga

Authors
  1. Daniela Sánchez
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  2. Patricia Melin
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  3. Juan Carpio
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  4. Hector Puga
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Corresponding author

Correspondence to Daniela Sánchez .

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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Sánchez, D., Melin, P., Carpio, J., Puga, H. (2017). Comparison of Optimization Techniques for Modular Neural Networks Applied to Human Recognition. 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_15

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  • DOI: https://doi.org/10.1007/978-3-319-47054-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47053-5

  • Online ISBN: 978-3-319-47054-2

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