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An Individual Adaptive Gain Parameter Backpropagation Algorithm for Complex-Valued Neural Networks

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

The complex-valued backpropagation algorithm has been widely used. However, the local minima problem usually occurs in the process of learning. We proposed an individual adaptive gain parameter backpropagation algorithm for complex-valued neural network to solve this problem. We specified the gain parameter of the sigmoid function in the hidden layer for each learning pattern. The proposed algorithm is tested by benchmark problem. The simulation results show that it is capable of preventing the complex-valued network learning from sticking into the local minima.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Toyama Prefectural University, 939-0398, Imizu Shi, Toyama, Japan

    Songsong Li & Toshimi Okada

  2. Faculty of Engineering, Toyama University, 930-8555, 3190 Gofuku, Toyama, Japan

    Xiaoming Chen & Zheng Tang

  3. Faculty of Information Science and Engineering, Shenyang University of Technology, 110023, 58 Xinghua Street, Tiexi, Shenyang, China

    Xiaoming Chen

Authors
  1. Songsong Li
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  2. Toshimi Okada
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  3. Xiaoming Chen
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  4. Zheng Tang
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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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Li, S., Okada, T., Chen, X., Tang, Z. (2006). An Individual Adaptive Gain Parameter Backpropagation Algorithm for Complex-Valued Neural Networks. 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 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_82

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  • DOI: https://doi.org/10.1007/11759966_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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