STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS

doi:10.1016/S0252-9602(15)30044-8

摘要/Abstract

摘要：

In this paper, global robust stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters is considered. A novel Linear matrix inequality(LMI) based stability criterion is obtained to guarantee the asymptotic stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters. The results are derived by using the Lyapunov functional technique, Lipchitz condition and S-procuture. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. Our results are also compared with results discussed in [31] and [34] to show the effectiveness and conservativeness.

关键词: Lyapunov functional, linear matrix inequality, Markovian jumping parameters, recurrent neural networks

Abstract:

Key words: Lyapunov functional, linear matrix inequality, Markovian jumping parameters, recurrent neural networks

中图分类号:

34K20

M. SYED ALI. STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS[J]. 数学物理学报(英文版), 2015, 35(5): 1122-1136.

M. SYED ALI. STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS[J]. Acta mathematica scientia,Series B, 2015, 35(5): 1122-1136.

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