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Wavelet Neural Networks for Nonlinear Time Series Analysis

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Book cover Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

A simple model based on the combination of neural network and wavelet techniques named wavelet neural network (WNN) is proposed. Thanks to the time-frequency analysis feature of wavelet, a selection method that takes into account the domain of input space where the wavelets are not zero is used to initialize the translation and dilation parameters. A proper choice of initialize parameters is found to be crucial in achieving adequate training. Training algorithms for feedback WNN is discussed too. Results obtained for a nonlinear processes is presented to test the effectiveness of the proposed method. The simulation result shows that the model is capable of producing a reasonable accuracy within several steps.

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References

  1. Yacine, O., Gerard, D.: Initialization by Selection for Wavelet Network Training. Neurocomputing 34, 131–143 (2000)

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Author information

Authors and Affiliations

  1. Dep. of Basics, Dalian Naval Academy, 116018, China

    Bo Zhou

  2. Dep. of Navigation, Dalian Naval Academy, 116018, China

    Aiguo Shi, Feng Cai & Yongsheng Zhang

Authors
  1. Bo Zhou
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  2. Aiguo Shi
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  3. Feng Cai
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  4. Yongsheng Zhang
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhou, B., Shi, A., Cai, F., Zhang, Y. (2004). Wavelet Neural Networks for Nonlinear Time Series Analysis. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_68

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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