{"title":"A Method of Robust Stabilization for the Delay Neural Networks with Nonlinear Perturbations","authors":"Ruliang Wang, Hong Lei, Jin Wang","doi":"10.1109/WGEC.2009.126","DOIUrl":null,"url":null,"abstract":"In this paper, we consider a class of time-delay dynamical systems with nonlinear perturbation. The nonlinear perturbation functions are assumed bounded. we provide a robust stabilization criterion via designing a memoryless state feedback controller for the time-delay dynamical neural networks with nonlinear perturbation. The sufficient criterion is given in terms of linear matrix inequality (LMI). The checking for robust stabilization of time-delay dynamical neural networks with nonlinear perturbation by our result can be carried out rather simply, and convenient for the application. The applicability of our results is demonstrated by means of two specific examples.","PeriodicalId":277950,"journal":{"name":"2009 Third International Conference on Genetic and Evolutionary Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Third International Conference on Genetic and Evolutionary Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WGEC.2009.126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper, we consider a class of time-delay dynamical systems with nonlinear perturbation. The nonlinear perturbation functions are assumed bounded. we provide a robust stabilization criterion via designing a memoryless state feedback controller for the time-delay dynamical neural networks with nonlinear perturbation. The sufficient criterion is given in terms of linear matrix inequality (LMI). The checking for robust stabilization of time-delay dynamical neural networks with nonlinear perturbation by our result can be carried out rather simply, and convenient for the application. The applicability of our results is demonstrated by means of two specific examples.