{"title":"延迟神经网络指数稳定性的进一步结果","authors":"Xiaofan Liu, Xinge Liu, Meilan Tang","doi":"10.1109/FSKD.2016.7603279","DOIUrl":null,"url":null,"abstract":"This paper considers exponential stability of delayed neural networks(NNs). Based on some novel integral inequalities and a modified Lyapunov-Krasovskii functional(LKF), further result on delay-dependent exponential stability is obtained for the considered delayed neural networks in form of linear matrix inequality(LMI). The effectiveness of our result in this paper is also demonstrated by a numerical example.","PeriodicalId":373155,"journal":{"name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Further results on exponential stability of delayed neural networks\",\"authors\":\"Xiaofan Liu, Xinge Liu, Meilan Tang\",\"doi\":\"10.1109/FSKD.2016.7603279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers exponential stability of delayed neural networks(NNs). Based on some novel integral inequalities and a modified Lyapunov-Krasovskii functional(LKF), further result on delay-dependent exponential stability is obtained for the considered delayed neural networks in form of linear matrix inequality(LMI). The effectiveness of our result in this paper is also demonstrated by a numerical example.\",\"PeriodicalId\":373155,\"journal\":{\"name\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2016.7603279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2016.7603279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Further results on exponential stability of delayed neural networks
This paper considers exponential stability of delayed neural networks(NNs). Based on some novel integral inequalities and a modified Lyapunov-Krasovskii functional(LKF), further result on delay-dependent exponential stability is obtained for the considered delayed neural networks in form of linear matrix inequality(LMI). The effectiveness of our result in this paper is also demonstrated by a numerical example.
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