研究了一类时滞细胞神经网络的指数稳定性问题,利用Razumikhin定理和线性不等式技术得到新的全局指数稳定性准则.与其他方法不同之处在于,对神经网络模型的“线性化”,将神经网络模型变成一个线性时变的系统.所获的条件具有较少的保守性.最后用1个数值例子说明文中所得的结果是有效的.
This paper considers the problems of global exponential stability for a general class of neural networks with time delays,a new criterion ensuring global exponential stability is obtained by utilizing Razumikhin theorem and the linear matrix inequality (LMI) technique.Distinct difference from other analytical approaches lies in "linearization" of the neural network model,by which the considered neural network model is transformed into a linear time-variant system.The obtained conditions show to be less conservative and restrictive.A numerical simulation is given to illustrate the validity of our results.