Stability analysis of switched stochastic neural networks with time-varying delays

Xiaotai Wu; Yang Tang; Wenbing Zhang

doi:10.1016/j.neunet.2013.12.001

Stability analysis of switched stochastic neural networks with time-varying delays

Neural Netw. 2014 Mar:51:39-49. doi: 10.1016/j.neunet.2013.12.001. Epub 2013 Dec 9.

Authors

Xiaotai Wu¹, Yang Tang², Wenbing Zhang³

Affiliations

¹ School of Mathematics, Anhui Polytechnic University, Wuhu 241000, PR China.
² Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany; Institute of Physics, Humboldt University of Berlin, Berlin 12489, Germany. Electronic address: tangtany@gmail.com.
³ Department of Mathematics, Yangzhou University, Jiangsu 225009, PR China.

PMID: 24365535
DOI: 10.1016/j.neunet.2013.12.001

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.

Keywords: Exponential stability; Linear matrix inequality; Neural networks; Switched systems; Time-varying delays.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computer Simulation
Linear Models
Neural Networks, Computer*
Stochastic Processes*
Time Factors