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Stability Analysis of a General Class of Continuous-Time Recurrent Neural Networks

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5551))

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Abstract

This paper presents the results of stability analysis of a general class of continuous-time recurrent neural networks. The new stability results includes sufficient conditions for global asymptotic stability. With weaker conditions and less restrictive activation functions, the new stability results improve and extend existing ones. Discussions and examples are given to illustrate and compare the new results with the existing ones.

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Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Hubei Normal University, Huangshi, Hubei, 435002, China

    Chaojin Fu

  2. School of Automation, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, 510665, China

    Zhongsheng Wang

Authors
  1. Chaojin Fu
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  2. Zhongsheng Wang
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering, Stevens Institute of Technology, NJ 07030, Hoboken, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering, South Dakota School of Mines & Technology, 501 E. St. Joseph Street, Rapid City, SD 57701, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Fu, C., Wang, Z. (2009). Stability Analysis of a General Class of Continuous-Time Recurrent Neural Networks. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_40

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  • DOI: https://doi.org/10.1007/978-3-642-01507-6_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01506-9

  • Online ISBN: 978-3-642-01507-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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