Robust extended dissipativity analysis for Markovian jump discrete-time delayed stochastic singular neural networks

Abstract

This paper is concerned with the problem of extended dissipativity analysis for delayed uncertain discrete-time singular neural networks (DTSNNs) having Markovian jump parameters and stochastic behavior. This paper demands to derive delay-dependent sufficient conditions such that the DTSNNs to be regular and causal, and is to find the stability nature and the robustness of the performance measures in the mean square sense. Based on Lyapunov–Krasovskii functional method and Cauchy–Schwartz-based summation inequality technique, a sufficient condition to guarantee an extended dissipativity performance and stability criterion for uncertain stochastic DTSNNs is presented in terms of linear matrix inequalities. Finally, numerical examples are provided to illustrate the advantages and improvements of the proposed method.