{"title":"一类具有非线性齐次激活函数的时滞神经网络的稳定性分析","authors":"Man Wang, Boshan Chen","doi":"10.1109/ICICIP.2015.7388139","DOIUrl":null,"url":null,"abstract":"The problem of asymptotic stability analysis is investigated about a class of delayed neural networks with homogeneous right-hands sides. Under the assumption that the trivial solution of delay free system is asymptotically stable and the activation functions are homogeneous, it is proved that the zero solution of delayed neural network is asymptotically stable for arbitrary nonnegative delay. By constructing a Lyapunov function and employing the nature of homogenous function, a new delay-independent asymptotically stability condition of the neural network with delay is obtained. At the end of the article, an appropriate numerical example which can demonstrate the effectiveness of the main result will be given.","PeriodicalId":265426,"journal":{"name":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stability Analysis for a class of delayed neural networks with nonlinear homogeneous activation functions\",\"authors\":\"Man Wang, Boshan Chen\",\"doi\":\"10.1109/ICICIP.2015.7388139\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of asymptotic stability analysis is investigated about a class of delayed neural networks with homogeneous right-hands sides. Under the assumption that the trivial solution of delay free system is asymptotically stable and the activation functions are homogeneous, it is proved that the zero solution of delayed neural network is asymptotically stable for arbitrary nonnegative delay. By constructing a Lyapunov function and employing the nature of homogenous function, a new delay-independent asymptotically stability condition of the neural network with delay is obtained. At the end of the article, an appropriate numerical example which can demonstrate the effectiveness of the main result will be given.\",\"PeriodicalId\":265426,\"journal\":{\"name\":\"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIP.2015.7388139\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2015.7388139","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stability Analysis for a class of delayed neural networks with nonlinear homogeneous activation functions
The problem of asymptotic stability analysis is investigated about a class of delayed neural networks with homogeneous right-hands sides. Under the assumption that the trivial solution of delay free system is asymptotically stable and the activation functions are homogeneous, it is proved that the zero solution of delayed neural network is asymptotically stable for arbitrary nonnegative delay. By constructing a Lyapunov function and employing the nature of homogenous function, a new delay-independent asymptotically stability condition of the neural network with delay is obtained. At the end of the article, an appropriate numerical example which can demonstrate the effectiveness of the main result will be given.