ROBUST ASYMPTOTICAL STABILITY FOR UNCERTAIN STOCHASTIC NEURAL NETWORKS WITH DISCRETE AND DISTRIBUTED DELAYS

摘要：

This paper investigates the problem of robust asymptotical stability for uncertain stochastic neural networks with discrete and distributed delays. Based on Lyapunov-Krasovskii functional and stochastic analysis method, new stability criteria is presented in terms of linear matrix inequalities to guarantee stochastic neural networks to be robustly asymptotically stable for all admissible parameter uncertainties, The criteria can be checked by utilizing the Matlab LMI toolbox. Two numerical examples are provided to demonstrate the feasibility of the proposed robust asymptotical stability criteria.

关键词： Stochastic neural networks Time delays Robust asymptotical stability Linear matriz inequalities Norm-bounded uncertainties

作者: SHU-YUN WANG SHAO-YING WANG GUO-GANG LI ZHI-FENG GAO

作者单位: Department of Mathematics, College of Handan, Handan 056004, China College of Science, Hebei University of Science and Technology, Shijiazhuang 050018, China College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 21001

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 815-819

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）