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A Gravitational Search Algorithm for Optimization of Modular Neural Networks in Pattern Recognition

  • Beatriz González
  • Fevrier Valdez
  • Patricia Melin
  • German Prado-Arechiga
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 574)

Abstract

The Gravitational Search Algorithm (GSA) is a novel heuristic optimization method based on the laws of gravity and mass interactions. We described in this paper a gravitational search algorithm to optimize the architecture of the modular neural network for recognition of medical images. In this case, we are using an echocardiograms database. Results obtained with this database are good; in this case the best learning algorithm was scaled conjugate gradient (SCG) with 90.27 % recognition rate comparing with gradient descent with adaptive learning rate backpropagation (GDA) with 84.72 %.

Keywords

Modular neural network Gravitational search algorithm Pattern recognition Echocardiograms GSA 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Beatriz González
    • 1
  • Fevrier Valdez
    • 1
  • Patricia Melin
    • 1
  • German Prado-Arechiga
    • 2
  1. 1.Tijuana Institute of TechnologyTijuanaMexico
  2. 2.Excel Medical CenterTijuanaMexico

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