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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications pp 391–403Cite as

Filter Size Optimization on a Convolutional Neural Network Using FGSA

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

Abstract

This paper presents an approach to optimize the filter size of a convolutional neural network using the fuzzy gravitational search algorithm (FGSA). The FGSA method has been applied in others works to optimize traditional neural networks achieving good results; for this reason, is used in this paper to optimize the parameters of a convolutional neural network. The optimization of the convolutional neural network is used for the recognition and classification of human faces images. The presented model can be used in any image classification, and in this paper the optimization of convolutional neural network is applied in the CROPPED YALE database.

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Acknowledgements

We thank sour sponsor CONACYT & the Tijuana Institute of Technology for the financial support provided with the scholarship number 816488.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Yutzil Poma, Patricia Melin, Claudia I. González & Gabriela E. Martínez

Authors
  1. Yutzil Poma
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  2. Patricia Melin
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  3. Claudia I. González
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  4. Gabriela E. Martínez
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Corresponding author

Correspondence to Patricia Melin .

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Editors and Affiliations

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

    Oscar Castillo

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

    Patricia Melin

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

    Janusz Kacprzyk

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Poma, Y., Melin, P., González, C.I., Martínez, G.E. (2020). Filter Size Optimization on a Convolutional Neural Network Using FGSA. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_29

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