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Training Method for a Feed Forward Neural Network Based on Meta-heuristics

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Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 82))

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Abstract

This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian-Cauchy PSO Neural Network converges faster and is immune to local minima.

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References

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Acknowledgments

This work was supported by Petronas Corporation, Petroleum Research Fund (PRF) No. 0153AB-A33.

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Authors and Affiliations

  1. Graduate School of Production, Information and Systems, Waseda University, Tokyo, Japan

    Haydee Melo & Huiming Zhang

  2. Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Pandian Vasant

  3. Deparment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Junzo Watada

Authors
  1. Haydee Melo
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  2. Huiming Zhang
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  3. Pandian Vasant
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  4. Junzo Watada
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Corresponding author

Correspondence to Haydee Melo .

Editor information

Editors and Affiliations

  1. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Swinburne University of Technology, Hawthorn, Victoria, Australia

    Pei-Wei Tsai

  3. Universiti Teknologi Petronas, Teronoh, Malaysia

    Junzo Watada

  4. University of Canberra, Bruce, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Melo, H., Zhang, H., Vasant, P., Watada, J. (2018). Training Method for a Feed Forward Neural Network Based on Meta-heuristics. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-319-63859-1_46

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  • DOI: https://doi.org/10.1007/978-3-319-63859-1_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63858-4

  • Online ISBN: 978-3-319-63859-1

  • eBook Packages: EngineeringEngineering (R0)

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