title: Stability analysis of switched stochastic neural networks with time-varying delays.
abstract: This paper is concerned with the global exponential stability of switched stochastic neural networks with time-varying delays. Firstly, the stability of switched stochastic delayed neural networks with stable subsystems is investigated by utilizing the mathematical induction method, the piecewise Lyapunov function and the average dwell time approach. Secondly, by utilizing the extended comparison principle from impulsive systems, the stability of stochastic switched delayed neural networks with both stable and unstable subsystems is analyzed and several easy to verify conditions are derived to ensure the exponential mean square stability of switched delayed neural networks with stochastic disturbances. The effectiveness of the proposed results is illustrated by two simulation examples.
authors.affiliation: School of Mathematics, Anhui Polytechnic University, Wuhu 241000, PR China.
authors.forename: Xiaotai
authors.initials: X
authors.lastname: Wu
authors.affiliation: Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany; Institute of Physics, Humboldt University of Berlin, Berlin 12489, Germany. Electronic address: tangtany@gmail.com.
authors.forename: Yang
authors.initials: Y
authors.lastname: Tang
authors.affiliation: Department of Mathematics, Yangzhou University, Jiangsu 225009, PR China.
authors.forename: Wenbing
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016014:Linear Models; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D013997:Time Factors
keywords: Exponential stability; Linear matrix inequality; Neural networks; Switched systems; Time-varying delays