{"title":"具有离散和分布时变时滞的细胞神经网络的全局渐近稳定性准则","authors":"Bin Huang, Minghui Jiang, Ting Zhang","doi":"10.1109/IWACI.2010.5585133","DOIUrl":null,"url":null,"abstract":"In this paper, the global asymptotic stability for a class of uncertain delayed cellular neural networks with discrete and distributed time-varying delays (DCNNs) is considered. Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new criterion is derived to guarantee global asymptotic stability. A numerical example is illustrated to show the effectiveness of our results.","PeriodicalId":189187,"journal":{"name":"Third International Workshop on Advanced Computational Intelligence","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On global asymptotic stability criteria for cellular neural networks with discrete and distributed time-varying delays\",\"authors\":\"Bin Huang, Minghui Jiang, Ting Zhang\",\"doi\":\"10.1109/IWACI.2010.5585133\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the global asymptotic stability for a class of uncertain delayed cellular neural networks with discrete and distributed time-varying delays (DCNNs) is considered. Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new criterion is derived to guarantee global asymptotic stability. A numerical example is illustrated to show the effectiveness of our results.\",\"PeriodicalId\":189187,\"journal\":{\"name\":\"Third International Workshop on Advanced Computational Intelligence\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Workshop on Advanced Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWACI.2010.5585133\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Workshop on Advanced Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWACI.2010.5585133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On global asymptotic stability criteria for cellular neural networks with discrete and distributed time-varying delays
In this paper, the global asymptotic stability for a class of uncertain delayed cellular neural networks with discrete and distributed time-varying delays (DCNNs) is considered. Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, a new criterion is derived to guarantee global asymptotic stability. A numerical example is illustrated to show the effectiveness of our results.
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