{"title":"具有马尔可夫跳跃和时变混合时滞的离散随机神经网络的渐近稳定性判据","authors":"Hongjun Chu, Fang Wang, Lixin Gao","doi":"10.1109/CCDC.2010.5499090","DOIUrl":null,"url":null,"abstract":"The global asymptotical stability problem is considered for a class of discrete-time stochastic recurrent neural networks(NNs) with Markovian jumping parameters and time-varying mixed delays in this paper. The mixed time delays include discrete delays and distributed delays, and both are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. The neural networks have a finite number of modes, and the modes may jump from one to another according to a discrete-time Markov chain. Based on the Lyapunov method and stochastic analysis approach, delay-interval dependent stability criterion is obtained in terms of linear matrix inequality(LMI) and generalizes existing results. Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.","PeriodicalId":227938,"journal":{"name":"2010 Chinese Control and Decision Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An asymptotical stability criterion for discrete-time stochastic neural networks with Markovian jumping and time-varying mixed delays\",\"authors\":\"Hongjun Chu, Fang Wang, Lixin Gao\",\"doi\":\"10.1109/CCDC.2010.5499090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The global asymptotical stability problem is considered for a class of discrete-time stochastic recurrent neural networks(NNs) with Markovian jumping parameters and time-varying mixed delays in this paper. The mixed time delays include discrete delays and distributed delays, and both are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. The neural networks have a finite number of modes, and the modes may jump from one to another according to a discrete-time Markov chain. Based on the Lyapunov method and stochastic analysis approach, delay-interval dependent stability criterion is obtained in terms of linear matrix inequality(LMI) and generalizes existing results. Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.\",\"PeriodicalId\":227938,\"journal\":{\"name\":\"2010 Chinese Control and Decision Conference\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Chinese Control and Decision Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCDC.2010.5499090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Chinese Control and Decision Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2010.5499090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An asymptotical stability criterion for discrete-time stochastic neural networks with Markovian jumping and time-varying mixed delays
The global asymptotical stability problem is considered for a class of discrete-time stochastic recurrent neural networks(NNs) with Markovian jumping parameters and time-varying mixed delays in this paper. The mixed time delays include discrete delays and distributed delays, and both are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. The neural networks have a finite number of modes, and the modes may jump from one to another according to a discrete-time Markov chain. Based on the Lyapunov method and stochastic analysis approach, delay-interval dependent stability criterion is obtained in terms of linear matrix inequality(LMI) and generalizes existing results. Finally, a numerical example is given to demonstrate the effectiveness of the proposed results.