Absolute Exponential Stability of Generalized Dynamical Neural Networks

广义动态神经网络的绝对指数稳定性(英文)

This paper investigates the absolute exponential stability of generalized neural networks with a general class of partially Lipschitz continuous and monotone increasing activation functions. The main obtained result is that if the interconnection matrix T of the neural system satisfies that - T is an H matrix with nonnegative diagonal elements, then the neural system is absolutely exponentially stable(AEST). The Hopfield network, Cellular neural network and Bidirectional associative memory network are special cases of the network model considered in this paper. So this work gives some improvements to the previous ones.

本文研究了一类具偏李普希兹连续和单调增加激活函数的神经网络绝对指数稳定性问题 .所得结果归结为如果联接矩阵T的负矩阵是一个非负对角元的H矩阵 ,那么在任意输入向量和网络参数的条件下 ,所选激活函数只要是偏李普希兹连续且单调增加的 ,广义动态神经网络绝对指数稳定 .该广义动态神经网络包含常用的Hopfield神经网络 ,双向联想记忆神经网络和细胞神经网络作为其特殊情形 。