Improved Exponential Stability Criteria for Recurrent Neural Networks with Time-varying Discrete and Distributed Delays

作     者：Yuan-Yuan Wu Tao Li Yu-Qiang Wu 

作者机构：School of Automation Southeast University Nanjing 210096 PRC Department of Information and Communication Nanjing University of Information Science and Technology Nanjing 210044 PRC Institute of Automation Qufu Normal University Qufu 273165 PRC 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2010年第7卷第2期

页      面：199-204页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070104[理学-应用数学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (No.60674027,No.60974127) Key Project of Education Ministry of China (No.208074) 

主　　题：Neural networks time-varying delay exponential stability linear matrix inequalities (LMIs). 

摘      要：In this paper, the problem of the global exponential stability analysis is investigated for a class of recurrent neural networks （RNNs） with time-varying discrete and distributed delays. Due to a novel technique when estimating the upper bound of the derivative of Lyapunov functional, we establish new exponential stability criteria in terms of LMIs. It is shown that the obtained criteria can provide less conservative results than some existing ones. Numerical examples are given to show the effectiveness of the proposed results.