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Particle Swarm Algorithm for the Optimization of Modular Neural Networks in Pattern Recognition

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

Abstract

In this paper a Particle Swarm Algorithm (PSO) is applied for the optimization of modular neural networks. This method is used for optimizing modular neural network in medical image recognition (Echocardiograms). As echocardiograms, these images help to diagnose heart diseases by the specialists and so can reduce the number of deaths by disease. Simulation results show that the scaled conjugate gradient (trainscg method) offers much higher performance with an average 81.4% recognition rate and with the gradient descent with adaptive learning (traingda method) an average of 76.3% recognition rate.

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Acknowledgements

We like to express our gratitude to CONACYT, Tijuana Institute of Technology for the resources granted for the development of this research.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Beatriz Gonzalez, Patricia Melin & Fevrier Valdez

Authors
  1. Beatriz Gonzalez
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  2. Patricia Melin
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  3. Fevrier Valdez
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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

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Gonzalez, B., Melin, P., Valdez, F. (2020). Particle Swarm Algorithm for the Optimization of Modular Neural Networks in Pattern Recognition. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_5

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