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Training RBF Neural Network with Hybrid Particle Swarm Optimization

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

The particle swarm optimization (PSO) has been used to train neural networks. But the particles collapse so quickly that it exits a potentially dangerous stagnation characteristic, which would make it impossible to arrive at the global optimum. In this paper, a hybrid PSO with simulated annealing and Chaos search technique (HPSO) is adopted to solve this problem. The HPSO is proposed to train radial basis function (RBF) neural network. Benchmark function optimization and dataset classification problems (Iris, Glass, Wine and New-thyroid) experimental results demonstrate the effectiveness and efficiency of the proposed algorithm.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Haichang Gao, Boqin Feng & Yun Hou

  2. School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Li Zhu

Authors
  1. Haichang Gao
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  2. Boqin Feng
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  3. Yun Hou
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  4. Li Zhu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Gao, H., Feng, B., Hou, Y., Zhu, L. (2006). Training RBF Neural Network with Hybrid Particle Swarm Optimization. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_86

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  • DOI: https://doi.org/10.1007/11759966_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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