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Adaptive Training of Radial Basis Function Networks Using Particle Swarm Optimization Algorithm

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3610))

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Abstract

A novel methodology to determine the optimum number of centers and the network parameters simultaneously based on Particle Swarm Optimization (PSO) algorithm with matrix encoding is proposed in this paper. For tackling structure matching problem, a random structure updating rule is employed for determining the current structure at each epoch. The effectiveness of the method is illustrated through the nonlinear system identification problem.

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Author information

Authors and Affiliations

  1. School of Electric & Information Engineering, Tongji University, ShangHai, 200092, China

    Hongkai Ding, Yunshi Xiao & Jiguang Yue

Authors
  1. Hongkai Ding
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  2. Yunshi Xiao
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  3. Jiguang Yue
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Ding, H., Xiao, Y., Yue, J. (2005). Adaptive Training of Radial Basis Function Networks Using Particle Swarm Optimization Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_14

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  • DOI: https://doi.org/10.1007/11539087_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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