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International Conference on Hybrid Intelligent Systems

HIS 2017: Hybrid Intelligent Systems pp 282–293Cite as

A Grey Wolf Optimization Algorithm for Modular Granular Neural Networks Applied to Iris Recognition

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 734))

Abstract

In this paper a Modular Granular Neural Network (MGNN) optimization is proposed, where a grey wolf optimizer is proposed to design MGNNs architectures. The design of these architectures consists in to seek number of sub modules, number of hidden layers for each sub module with their respective number of neurons, learning method, error goal and percentage of data used for the training phase. This model is based on the percentage of data (in this work are images) used for the training phase to perform a selection of which are the optimal images to be used for that phase. The proposed method was applied to pattern recognition based on the iris biometrics.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin & Daniela Sánchez

Authors
  1. Patricia Melin
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  2. Daniela Sánchez
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Melin, P., Sánchez, D. (2018). A Grey Wolf Optimization Algorithm for Modular Granular Neural Networks Applied to Iris Recognition. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_29

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  • DOI: https://doi.org/10.1007/978-3-319-76351-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76350-7

  • Online ISBN: 978-3-319-76351-4

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