{"title":"一种改进的时变时滞递归神经网络的时滞相关稳定性准则","authors":"Zhuo Wang, Chenghong Wang, Su Song","doi":"10.1109/ISIC.2008.4635956","DOIUrl":null,"url":null,"abstract":"Combining the concept of sampling control with free weight matrix method, two delay-dependent asymptotic stability criteria are presented for the equilibrium point of a class of recurrent neural networks with time varying delay. The obtained results can be suitable for fast or slowly time-varying delayed recurrent neural networks, and larger upper bound of time delay can be admitted. Comparisons are made by a numerical example to show the effectiveness of the obtained results.","PeriodicalId":342070,"journal":{"name":"2008 IEEE International Symposium on Intelligent Control","volume":"493 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Delay-dependent Stability Criteria for Recurrent Neural Networks with Time-varying Delay\",\"authors\":\"Zhuo Wang, Chenghong Wang, Su Song\",\"doi\":\"10.1109/ISIC.2008.4635956\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Combining the concept of sampling control with free weight matrix method, two delay-dependent asymptotic stability criteria are presented for the equilibrium point of a class of recurrent neural networks with time varying delay. The obtained results can be suitable for fast or slowly time-varying delayed recurrent neural networks, and larger upper bound of time delay can be admitted. Comparisons are made by a numerical example to show the effectiveness of the obtained results.\",\"PeriodicalId\":342070,\"journal\":{\"name\":\"2008 IEEE International Symposium on Intelligent Control\",\"volume\":\"493 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Intelligent Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIC.2008.4635956\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Intelligent Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIC.2008.4635956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Delay-dependent Stability Criteria for Recurrent Neural Networks with Time-varying Delay
Combining the concept of sampling control with free weight matrix method, two delay-dependent asymptotic stability criteria are presented for the equilibrium point of a class of recurrent neural networks with time varying delay. The obtained results can be suitable for fast or slowly time-varying delayed recurrent neural networks, and larger upper bound of time delay can be admitted. Comparisons are made by a numerical example to show the effectiveness of the obtained results.