{"title":"一类时变时滞神经网络的全局渐近稳定性","authors":"T. Ensari, S. Arik, V. Tavsanoglu","doi":"10.1109/ISCAS.2004.1329934","DOIUrl":null,"url":null,"abstract":"This work presents a new sufficient condition for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with time varying delays. It is shown that the use of a more general type of Lyapunov-Krasovskii functional leads to establish global asymptotic stability of a larger class of delayed neural networks that the neural network model considered in some previous papers.","PeriodicalId":6445,"journal":{"name":"2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)","volume":"61 1","pages":"V-V"},"PeriodicalIF":0.0000,"publicationDate":"2004-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Global asymptotic stability of a class of neural networks with time varying delays\",\"authors\":\"T. Ensari, S. Arik, V. Tavsanoglu\",\"doi\":\"10.1109/ISCAS.2004.1329934\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a new sufficient condition for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with time varying delays. It is shown that the use of a more general type of Lyapunov-Krasovskii functional leads to establish global asymptotic stability of a larger class of delayed neural networks that the neural network model considered in some previous papers.\",\"PeriodicalId\":6445,\"journal\":{\"name\":\"2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)\",\"volume\":\"61 1\",\"pages\":\"V-V\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCAS.2004.1329934\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2004.1329934","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Global asymptotic stability of a class of neural networks with time varying delays
This work presents a new sufficient condition for the uniqueness and global asymptotic stability (GAS) of the equilibrium point for a larger class of neural networks with time varying delays. It is shown that the use of a more general type of Lyapunov-Krasovskii functional leads to establish global asymptotic stability of a larger class of delayed neural networks that the neural network model considered in some previous papers.