基于LMI的随机神经网络全局渐进稳定性

研究了一类变时滞的随机神经网络模型零解的全局渐进稳定性.基于Lyapunov稳定性理论,通过构造新型Lyapunov-Krasovskii泛函,利用线性矩阵不等式分析技巧,结合It^o微分公式,得到了保证网络的平衡解为均方意义下全局渐进稳定的判别条件.推广了一些已有的结果,并且更少保守.所的结论可用Matlab中的线性矩阵不等式工具箱进行计算来验证网络的稳定性,通过数值例子证明了结论的有效性和易用性.

LMI-Based Approach for Global Asymptotic Stability of Stochastic Neural Networks

This paper deals with the problem of stability for the equilibrium solution of a class of stochastic neural networks with hybrid time-delays.Based on Lyapunov stability theory and Ito^ formula,mean square exponential stability for the solutions of stochastic neural network is discussed.According to the hybrid delays in neural network,constructing new Lyapunov-Krasovskii functional,by means of method of matrix inequality analysis,new criteria is derived in terms of linear matrix inequalities,the conservatism of new conditions is less than that given by matrix norm.Linear matrix inequality can be readily checked by using the Matlab LMI Toolbox,such that the criteria is more practical.A numerical examples is provided to demonstrate the applicability and effectiveness of the proposed criteria.