title: On stabilization of stochastic Cohen-Grossberg neural networks with mode-dependent mixed time-delays and Markovian switching.
abstract: The globally exponential stabilization problem is investigated for a general class of stochastic Cohen-Grossberg neural networks with both Markovian jumping parameters and mixed mode-dependent time-delays. The mixed time-delays consist of both discrete and distributed delays. This paper aims to design a memoryless state feedback controller such that the closed-loop system is stochastically exponentially stable in the mean square sense. By introducing a new Lyapunov-Krasovskii functional that accounts for the mode-dependent mixed delays, stochastic analysis is conducted in order to derive delay-dependent criteria for the exponential stabilization problem. Three numerical examples are carried out to demonstrate the feasibility of our delay-dependent stabilization criteria.
authors.forename: Cheng-De
authors.initials: CD
authors.lastname: Zheng
authors.forename: Qi-He
authors.initials: QH
authors.lastname: Shan
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
authors.forename: Zhanshan
authors.initials: Z
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D013997:Time Factors