拉格朗日意义下带随机项的细胞神经网络的指数稳定判据

On Global Exponential Stability in Lagrange Sense for Recurrent Neural Networks with Stochastic

研究了拉格朗日意义下带随机项的细胞神经网络新的指数稳定判据.讨论的激励函数既非有界又非可微,通过建立新的Lyapunov-Krasovskii泛函,以线性矩阵不等式的形式得到了几个判定神经网络系统指数稳定的判据;得到了更一般的全局指数收敛集的估计方法;最后用算例验证了所提判据的有效性.

The global exponential stability in Lagrange sense for recurrent neural networks with stochastic and multiple discrete delays have been investigated. Based on the assumption that the activation functions are neither bounded nor monotonous or differentiable, several algebraic criterions in linear matrix inequali ty form for the global exponential stability in a Lagrange sense of the neural networks are obtained by vir tue of Lyapunov-Krasovskii functions. Meanwhile, the estimations of the globally exponentially attractive sets have been given out. The results derive here are more general than that of the existing reference. Fi nally, a numerical example is provided to illustrate the applicability of the stability results.