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Improved Stability Criteria for Discrete-Time Stochastic Neural Networks with Randomly Time-Varying Delays

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Bio-Inspired Computing and Applications (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6840))

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Abstract

This paper investigates the problem of exponential stability for a class of discrete-time stochastic neural network with randomly time-varying delays. Compared with some previous results, the new conditions obtained in this paper are less conservative. Finally, a numerical example is exploited to show the usefulness of the results derived.

This work was supported by the National Natural Science Foundation of China (Grant No. 60736029) and the National Basic Research Program of China(2010CB732501).

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, University of Electronic Science and Technology of China, 611731, Chengdu, P.R. China

    Mengzhuo Luo & Shouming Zhong

  2. Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, 610054, Chengdu, Sichuan, P.R. China

    Shouming Zhong

Authors
  1. Mengzhuo Luo
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  2. Shouming Zhong
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. School of Electrical, Computer & Telecommunications Engineering, University of Wollongong, 2522, P.O. Box, North Wollongong, NSW, Australia

    Prashan Premaratne

  4. School of Computer Science and Engineering, Inha University, 402-751, Inchon, Korea

    Kyungsook Han

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© 2012 Springer-Verlag Berlin Heidelberg

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Luo, M., Zhong, S. (2012). Improved Stability Criteria for Discrete-Time Stochastic Neural Networks with Randomly Time-Varying Delays. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-24553-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24552-7

  • Online ISBN: 978-3-642-24553-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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