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A New On-Line Modeling Approach to Nonlinear Dynamic Systems

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

An improved radial basis function neural network (IRBFNN) with unsymmetrical Gaussian function is presented to simplify the structure of RBFNN. The improved resource allocating network (IRAN) is developed to design IRBFNN online for nonlinear dynamic system modeling, integrating the typical resource allocating network (RAN) with merging method for similar hidden units, deleting strategy for redundant hidden units, and LMS learning algorithm with moving data window for output link weights of network. The proposed approach can effectively improve the precision and generalization of IRBFNN. The combination of IRBFNN and IRAN is competent for the online modeling of nonlinear dynamic systems. The feasibility and effectiveness of the modeling method have been demonstrated by simulations.

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Author information

Authors and Affiliations

  1. College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China

    Shirong Liu & Qijiang Yu

  2. Research Institute of Automation, East China University of Science and Technology, Shanghai, 200237, China

    Qijiang Yu & Jinshou Yu

Authors
  1. Shirong Liu
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  2. Qijiang Yu
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  3. Jinshou Yu
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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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Liu, S., Yu, Q., Yu, J. (2006). A New On-Line Modeling Approach to Nonlinear Dynamic Systems. 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 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_114

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  • DOI: https://doi.org/10.1007/11760023_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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