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Neural Computing and Applications
Published in: Neural Computing and Applications 2/2012

01-03-2012 | Original Article

Pinning a stochastic neural network to the synchronous state

Authors: Tao He, Jigen Peng, Jikai Lei

Published in: Neural Computing and Applications | Issue 2/2012

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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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Metadata
Title
Pinning a stochastic neural network to the synchronous state
Authors
Tao He
Jigen Peng
Jikai Lei
Publication date
01-03-2012
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 2/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-010-0426-0

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