Delay-Dependent Robust Exponential Stability of Impulsive Markovian Jumping Reaction-Diffusion Cohen-Grossberg Neural Networks

Authors:
Yonggui Kao;Changhong Wang;Lin Zhang
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, People's Republic of China 150001 and Shandong Provincial Key Laboratory of Industrial Control Techno ...;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, People's Republic of China 150001;Division of Adult & Graduate Studies, Eastern Nazarene College, Quicy, USA
Venue:
Neural Processing Letters
Year:
2013

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Abstract

This paper is devoted to investigating delay-dependent robust exponential stability for a class of Markovian jump impulsive stochastic reaction-diffusion Cohen-Grossberg neural networks (IRDCGNNs) with mixed time delays and uncertainties. The jumping parameters, determined by a continuous-time, discrete-state Markov chain, are assumed to be norm bounded. The delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing a Lyapunov---Krasovskii functional, and using poincarè inequality and the mathematical induction method, several novel sufficient criteria ensuring the delay-dependent exponential stability of IRDCGNNs with Markovian jumping parameters are established. Our results include reaction-diffusion effects. Finally, a Numerical example is provided to show the efficiency of the proposed results.