基于LMI的随机时滞神经网络的全局渐近稳定性分析

Global Asymptotic Stability Analysis of Class of Stochastic Delay Neural Networks

研究了一类时变时滞与分布时滞的随机神经网络模型的全局渐近稳定性,该模型考虑了神经网络的随机扰动性.通过构造适当的Lyapunov泛函,以线性矩阵不等式的形式给出了系统全局渐近稳定的充分条件.最后,数值算例说明了结果的正确性.

Global asymptotic stability for a class of stochastic neural networks with time-varying delays and distributed delay was studied. By constructing suitable Lyapunov functionals and combining with ma- trix inequality technique, a simple sufficient condition was presented for global asymptotic stability in the mean square of stochastic neural networks with time-varying delays and distributed delay. By LMI toobox, it demonstrated the usefulness of the new proposed global asymptotic stability criteria.