{"title":"具有不确定时变延迟的细胞神经网络的指数稳定性","authors":"Xueli Wu, Xuan Lv, Hua Meng, Yang Li","doi":"10.1109/ICNC.2009.384","DOIUrl":null,"url":null,"abstract":"A novel method is proposed in this note for exponential stability of cellular neural networks with uncertain and time-varying delay. New delay-dependent exponential stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). The sufficient conditions on exponential stability established in this paper, which are easily verifiable, have a wider adaptive range. Finally, a numerical example is given to demonstrate the effect of the proposed method.","PeriodicalId":235382,"journal":{"name":"2009 Fifth International Conference on Natural Computation","volume":"183 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Exponential Stability of Cellular Neural Networks with Uncertain and Time-Varying Delay\",\"authors\":\"Xueli Wu, Xuan Lv, Hua Meng, Yang Li\",\"doi\":\"10.1109/ICNC.2009.384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel method is proposed in this note for exponential stability of cellular neural networks with uncertain and time-varying delay. New delay-dependent exponential stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). The sufficient conditions on exponential stability established in this paper, which are easily verifiable, have a wider adaptive range. Finally, a numerical example is given to demonstrate the effect of the proposed method.\",\"PeriodicalId\":235382,\"journal\":{\"name\":\"2009 Fifth International Conference on Natural Computation\",\"volume\":\"183 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Fifth International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2009.384\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Fifth International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2009.384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exponential Stability of Cellular Neural Networks with Uncertain and Time-Varying Delay
A novel method is proposed in this note for exponential stability of cellular neural networks with uncertain and time-varying delay. New delay-dependent exponential stability conditions of cellular neural network with time-varying delay is presented by constructing Lyapunov function and using linear matrix inequality (LMI). The sufficient conditions on exponential stability established in this paper, which are easily verifiable, have a wider adaptive range. Finally, a numerical example is given to demonstrate the effect of the proposed method.