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Feedforward Wavelet Neural Network and Multi-variable Functional Approximation

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Computational and Information Science (CIS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3314))

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Abstract

In this paper, a novel WNN, multi-input and multi-output feedforward wavelet neural network is constructed. In the hidden layer, wavelet basis functions are used as activate function instead of the sigmoid function of feedforward network. The training formulas based on BP algorithm are mathematically derived and training algorithm is presented. A numerical experiment is given to validate the application of this wavelet neural network in multi-variable functional approximation.

This work is supported by 863 Project of China (No.2002AA234021) and 973 Project of China (No. 2002CB512800).

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References

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Author information

Authors and Affiliations

  1. Biomedical Engineering Department, School of Life Science and Technology, Shanghai Jiaotong University, Shanghai, 200240, P.R. China

    Jing Zhao & Jianhua Luo

  2. Basic Department,  

    Jing Zhao

  3. Department of Automation, Logistical Engineering University, Chongqing, 400016, P.R. China

    Wang Chen

Authors
  1. Jing Zhao
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  2. Wang Chen
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  3. Jianhua Luo
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Editor information

Editors and Affiliations

  1. School of Electronic Science and Technology, Anhui University, Hefei, Anhui, China

    Jun Zhang

  2. College of Science, Donghua University, 1882 Yan’an Xilu Road, 20051, Shanghai, China

    Ji-Huan He

  3. BASICS, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200030, Shanghai, China

    Yuxi Fu

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhao, J., Chen, W., Luo, J. (2004). Feedforward Wavelet Neural Network and Multi-variable Functional Approximation. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_6

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  • DOI: https://doi.org/10.1007/978-3-540-30497-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24127-0

  • Online ISBN: 978-3-540-30497-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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