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Dynamic Analysis of Stochastic Recurrent Neural Networks

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Abstract

This paper addresses the issue of pth moment exponential stability of stochastic recurrent neural networks (SRNN) with time-varying interconnections and delays. With the help of the Dini derivative of the expectation of V(t, X(t)) “along” the solution X(t) of the model and the technique of Halanay-type inequality, some novel sufficient conditions on pth moment exponential stability of the trivial solution has been established. Conclusions of the development as presented in this paper have gone beyond some published results and are helpful to design stability of networks when stochastic noise is taken into consideration. An example is also given to illustrate the effectiveness of our results.

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Authors and Affiliations

  1. College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan, 410076, P.R. China

    Chuangxia Huang & Ping Chen

  2. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, P.R. China

    Chuangxia Huang & Yigang He

Authors
  1. Chuangxia Huang
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  2. Yigang He
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  3. Ping Chen
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Correspondence to Chuangxia Huang.

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Huang, C., He, Y. & Chen, P. Dynamic Analysis of Stochastic Recurrent Neural Networks. Neural Process Lett 27, 267–276 (2008). https://doi.org/10.1007/s11063-008-9075-z

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  • DOI: https://doi.org/10.1007/s11063-008-9075-z

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