Robust exponential stability of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays.
This paper is concerned with the problem of exponential stability for a class of markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays and known or unknown parameters. The jumping parameters are determined by a continuous-time, discrete-state Markov chain, and the mixed time delays under consideration comprise both time-varying delays and continuously distributed delays. To the best of the authors' knowledge, till now, the exponential stability problem for this class of generalized neural networks has not yet been solved since continuously distributed delays are considered in this paper. The main objective of this paper is to fill this gap. By constructing a novel Lyapunov-Krasovskii functional, and using some new approaches and techniques, several novel sufficient conditions are obtained to ensure the exponential stability of the trivial solution in the mean square. The results presented in this paper generalize and improve many known results. Finally, two numerical examples and their simulations are given to show the effectiveness of the theoretical results.
Zhu
Quanxin
Q
Department of Mathematics, Ningbo University, Ningbo 315211, China. zqx22@126.com
Cao
Jinde
J
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D013997:Time Factors