{"title":"随机延迟神经网络的稳定性和L2性能分析。","authors":"Yun Chen, Wei Xing Zheng","doi":"10.1109/TNN.2011.2163319","DOIUrl":null,"url":null,"abstract":"<p><p>This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.</p>","PeriodicalId":13434,"journal":{"name":"IEEE transactions on neural networks","volume":"22 10","pages":"1662-8"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TNN.2011.2163319","citationCount":"28","resultStr":"{\"title\":\"Stability and L2 performance analysis of stochastic delayed neural networks.\",\"authors\":\"Yun Chen, Wei Xing Zheng\",\"doi\":\"10.1109/TNN.2011.2163319\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.</p>\",\"PeriodicalId\":13434,\"journal\":{\"name\":\"IEEE transactions on neural networks\",\"volume\":\"22 10\",\"pages\":\"1662-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/TNN.2011.2163319\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on neural networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TNN.2011.2163319\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2011/8/12 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on neural networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TNN.2011.2163319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2011/8/12 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Stability and L2 performance analysis of stochastic delayed neural networks.
This brief focuses on the robust mean-square exponential stability and L(2) performance analysis for a class of uncertain time-delay neural networks perturbed by both additive and multiplicative stochastic noises. New mean-square exponential stability and L(2) performance criteria are developed based on the delay partition Lyapunov-Krasovskii functional method and generalized Finsler lemma which is applicable to stochastic systems. The analytical results are established without involving any model transformation, estimation for cross terms, additional free-weighting matrices, or tuning parameters. Numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing ones.