Mean square stability of recurrent neural networks with random delay and Markovian switching

ZHU En-wen^1,2, WANG Yong³, ZHANG Han-jun², ZOU Jie-zhong⁴

1.School of Mathematics and Computational Science,Changsha University of Science and Technology,Changsha 410076,China;2.School of Mathematics and Computational Science,Xiangtan University,Xiangtan 411105,China;3.Dept.of Mathematics,Harbin Institute of Technology, Harbin 150001,China;4.School of Mathematics,Central South University,Changsha 410075,China



Abstract:

To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.

Key words:  recurrent neural networks  mean-square stability  random delay  Markovian switching  linear matrix inequality

DOI£º10.11916/j.issn.1005-9113.2009.05.017

Clc Number:O781;O734

Fund: