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Filter Size Optimization on a Convolutional Neural Network Using FGSA

  • Yutzil Poma
  • Patricia MelinEmail author
  • Claudia I. González
  • Gabriela E. Martínez
Chapter
  • 43 Downloads
Part of the Studies in Computational Intelligence book series (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.

Keywords

FGSA Convolutional neural network Optimization Neural network Pattern recognition 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yutzil Poma
    • 1
  • Patricia Melin
    • 1
    Email author
  • Claudia I. González
    • 1
  • Gabriela E. Martínez
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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