Stability of discrete Hopfield neural networks with delay

作     者：Ma Runnian 1,2 , Lei Sheping3 & Liu Naigong41. Telecommunication Engineering Inst., Air Force Engineering Univ., Xi’an 710071, P. R. China 2. Key Lab of Information Sciences and Engineering, Dalian Univ., Dalian 111662, P. R. China 3. School of Humanity Law and Economics, Northwestern Polytechnical Univ., Xi’an 710072, P. R. China 4. Science Inst., Air Force Engineering Univ., Xi’an 710051, P. R. China 

作者机构：Telecommunication Engineering Inst. Air Force Engineering Univ. XT an 710071 P. R. China Key Lab of Information Sciences and Engineering Dalian Univ. Dalian 111662 P. R. China School of Humanity Law and Economics Northwestern Polytechnical Univ. XT an 710072 P. R. China Science Inst. Air Force Engineering Univ. XT an 710051 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2005年第16卷第4期

页      面：937-940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)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081104[工学-模式识别与智能系统] 08[工学] 0802[工学-机械工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：ThisprojectwassupportedbyChinaPostdoctoralScienceFoundation(2003033516) andpartlysupportedbyOpenFoundationofUniversityKeyLabofInformationSciencesandEngineering DalianUniversity 

主　　题：discrete Hopfield neural network with delay stability limit cycle. 

摘      要：Discrete Hopfield neural network with delay is an extension of discrete Hopfield neural network. As it is well known, the stability of neural networks is not only the most basic and important problem but also foundation of the network＇s applications. The stability of discrete HJopfield neural networks with delay is mainly investigated by using Lyapunov function. The sufficient conditions for the networks with delay converging towards a limit cycle of length 4 are obtained. Also, some sufficient criteria are given to ensure the networks having neither a stable state nor a limit cycle with length 2. The obtained results here generalize the previous results on stability of discrete Hopfield neural network with delay and without delay.