ASYMPTOTICAL STABILITY OFNON-AUTONOMOUS DISCRETE-TIME NEURAL NETWORKS WITH GENERALIZED INPUT-OUTPUT FUNCTION

• 作者: 王军平 郭德典 阮炯
• 来源期刊: 微分方程年刊：英文版
• 学科: 工学
• 关键词: 输入 输出 人工神经网络模型 离散 定点 渐进 稳定性
• 年，卷（期）: 2004,（2）
• 页码范围: 155-167
• 页数: 13页
• 分类号: TP183

摘要

In this paper, we first introduce the model of discrete-time neural networks with generalized input-output function and present a proof of the existence of a fixed point by Schauder fixed-point principle. Secondly, we study the uniformly asymptotical stability of equilibrium in non-autonomous discrete-time neural networks and give some sufficient conditions that guarantee the stability of it by using the converse theorem of Lyapunov function. Finally, several examples and numerical simulations are given to illustrate and reinforce our theories.