Improved Conditions for Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays
Improved Conditions for Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays作者机构：College of Communication and Control Engineering Jiangnan University Wuxi 214122
出 版 物：《Chinese Physics Letters》 (中国物理快报（英文版）)
年 卷 期：2008年第25卷第11期
页 面：3894-3897页
核心收录：
学科分类：080901[工学-物理电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：Supported by the National Natural Science Foundation of China under Grant No 60674026 the Natural Science Foundation of Jiangsu Province under Grant No BK2007016 and the Programme for Innovative Research Team of Jiangnan University
主 题：MATHEMATICAL physics -- Asymptotic theory NEURAL networks (Computer science) LYAPUNOV functions INEQUALITIES (Mathematics) NUMERICAL analysis SIMULATION methods & models
摘 要：The global asymptotic stability of delayed Cohen-Grossberg neural networks with impulses is investigated. Based on the new suitable Lyapunov functions and the Jacobsthal inequality, a set of novel sufficient criteria are derived for the global asymptotic stability of Cohen-Grossberg neural networks with time-varying delays and impulses. An illustrative example with its numerical simulations is given to demonstrate the effectiveness of the obtained results.
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