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Globally Exponential Stability Analysis and Estimation of the Exponential Convergence Rate for Neural Networks with Multiple Time Varying Delays

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Advances in Natural Computation (ICNC 2005)

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Abstract

Some sufficient conditions for the globally exponential stability of the equilibrium point of neural networks with multiple time varying delays are developed, and the estimation of the exponential convergence rate is presented. The obtained criteria are dependent on time delay, and consist of all the information on the neural networks. The effects of time delay and number of connection matrices of the neural networks on the exponential convergence rate are analyzed, which can give a clear insight into the relation between the exponential convergence rate and the parameters of the neural networks. Two numerical examples are used to demonstrate the effectiveness of the obtained the results.

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Author information

Authors and Affiliations

  1. Key Laboratory of Process Industry Automation, Ministry of Education, Northeastern University, Shenyang, 110004, P. R. China

    Huaguang Zhang & Zhanshan Wang

  2. Department of Information Engineering, Shenyang Ligong University, Shenyang, 110045, P. R. China

    Zhanshan Wang

Authors
  1. Huaguang Zhang
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  2. Zhanshan Wang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, H., Wang, Z. (2005). Globally Exponential Stability Analysis and Estimation of the Exponential Convergence Rate for Neural Networks with Multiple Time Varying Delays. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_8

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  • DOI: https://doi.org/10.1007/11539087_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28323-2

  • Online ISBN: 978-3-540-31853-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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