title: Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays.
abstract: In this letter, the global asymptotic stability analysis problem is considered for a class of stochastic Cohen-Grossberg neural networks with mixed time delays, which consist of both the discrete and distributed time delays. Based on an Lyapunov-Krasovskii functional and the stochastic stability analysis theory, a linear matrix inequality (LMI) approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square. It is shown that the addressed stochastic Cohen-Grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two LMIs are feasible, where the feasibility of LMIs can be readily checked by the Matlab LMI toolbox. It is also pointed out that the main results comprise some existing results as special cases. A numerical example is given to demonstrate the usefulness of the proposed global stability criteria.
authors.forename: Zidong
authors.initials: Z
authors.lastname: Wang
authors.forename: Yurong
authors.initials: Y
authors.lastname: Liu
authors.forename: Maozhen
authors.initials: M
authors.lastname: Li
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013269:Stochastic Processes; D013598:Systems Theory; D013997:Time Factors