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Integrated Radial Basis Function Networks with Adaptive Residual Subsampling Training Method for Approximation and Solving PDEs

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

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Abstract

This paper proposes a method for approximation and solving PDEs, based on integrated radial basis function networks (IRBFNs) with adaptive residual subsampling training algorithm. The Multiquadratic function is chosen as the transfer function of the neurons. The effectiveness of the method is demonstrated in numerical examples by approximating several functions and solving Burgers’ equation. The result of numerical experiments shows that the IRBFNs with the proposed adaptive procedure requires less neurons to attain the accuracy than direct radial basis function (DRBF) network.

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Author information

Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hong Chen & Li Kong

  2. Wuhan 2nd Ship Design and Research Institute, Wuhan, 430064, China

    Hong Chen

Authors
  1. Hong Chen
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  2. Li Kong
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Chen, H., Kong, L. (2009). Integrated Radial Basis Function Networks with Adaptive Residual Subsampling Training Method for Approximation and Solving PDEs. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_112

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_112

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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