title: Stability analysis of time-delay neural networks subject to stochastic perturbations.
abstract: This paper is concerned with the problem of mean-square exponential stability of uncertain neural networks with time-varying delay and stochastic perturbation. Both linear and nonlinear stochastic perturbations are considered. The main features of this paper are twofold: 1) Based on generalized Finsler lemma, some improved delay-dependent stability criteria are established, which are more efficient than the existing ones in terms of less conservatism and lower computational complexity; and 2) when the nonlinear stochastic perturbation acting on the system satisfies a class of Lipschitz linear growth conditions, the restrictive condition P < δI (or the similar ones) in the existing results can be relaxed under some assumptions. The usefulness of the proposed method is demonstrated by illustrative examples.
authors.forename: Yun
authors.initials: Y
authors.lastname: Chen
authors.forename: Wei Xing
authors.initials: WX
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes; D013997:Time Factors