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Convergence Analysis of Continuous-Time 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 energy function of continuous-time neural network has been analyzed for testing the existence of stationary points and the global convergence of network. The energy function always has only one stationary point which is a saddle point in the unconstrained space when the total conductance of neuron’s input is zero (G i = 0). However, the stationary points exist only inside the hypercube Rn ∈[0,1] when the total conductance of neuron’s input is not zero (G i ≠ 0). The Hessian matrix of the energy function is used for testing the global convergence of the network.

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Author information

Authors and Affiliations

  1. Faculty of Electrical & Electronic Engineering, Cheju National University, Jeju, 690-756, South Korea

    Min-Jae Kang & Ho-Chan Kim

  2. Department of Computer Engineering, Cheju National University, Jeju, 690-756, South Korea

    Farrukh A. Khan & Wang-Cheol Song

  3. Department of Electrical Engineering, University of Louisville, Louisville, KY, 40292, USA

    Jacek M. Zurada

Authors
  1. Min-Jae Kang
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  2. Ho-Chan Kim
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  3. Farrukh A. Khan
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  4. Wang-Cheol Song
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  5. Jacek M. Zurada
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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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Kang, MJ., Kim, HC., Khan, F.A., Song, WC., Zurada, J.M. (2006). Convergence Analysis of Continuous-Time 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_15

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  • DOI: https://doi.org/10.1007/11759966_15

  • 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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