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Pinning a stochastic neural network to the synchronous state

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Abstract

In this paper, the asymptotic stability of the pinning synchronous solution of stochastic neural networks with and without time-delays is analyzed. The delays are time-varying, and the uncertainties are norm-bounded that enter into all the parameters of network and control. The aim of this paper is not only to establish easily verifiable conditions under which the pinning synchronous solution of stochastic neural network is globally asymptotically stable but also to give a feasible way to offset the limitation of network itself in order to reach synchronization. In addition, a specific neurobiological network is also introduced, and some numerical examples are provided to illustrate the applicability of the proposed criteria.

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Authors and Affiliations

  1. Department of Mathematics, Xi’an Jiaotong University, 710049, Xi’an, China

    Tao He & Jigen Peng

  2. Department of Computer Science and Technology, Xi’an Jiaotong University, 710049, Xi’an, China

    Jikai Lei

Authors
  1. Tao He
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  2. Jigen Peng
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  3. Jikai Lei
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Corresponding author

Correspondence to Tao He.

Additional information

This work was supported by the NCET and in part by the NSFC under the contact 10531030.

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He, T., Peng, J. & Lei, J. Pinning a stochastic neural network to the synchronous state. Neural Comput & Applic 21, 289–297 (2012). https://doi.org/10.1007/s00521-010-0426-0

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  • DOI: https://doi.org/10.1007/s00521-010-0426-0

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