{"title":"一类非线性系统的神经网络在线解耦","authors":"Xinli Li, Yan Bai, L. Yang","doi":"10.1109/WCICA.2006.1712900","DOIUrl":null,"url":null,"abstract":"Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system","PeriodicalId":375135,"journal":{"name":"2006 6th World Congress on Intelligent Control and Automation","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Neural Network Online Decoupling for a Class of Nonlinear System\",\"authors\":\"Xinli Li, Yan Bai, L. Yang\",\"doi\":\"10.1109/WCICA.2006.1712900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system\",\"PeriodicalId\":375135,\"journal\":{\"name\":\"2006 6th World Congress on Intelligent Control and Automation\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 6th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2006.1712900\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 6th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2006.1712900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural Network Online Decoupling for a Class of Nonlinear System
Aim at a class of nonlinear MIMO systems, the neural networks online decoupling algorithm is proposed. The elitist genetic algorithms and hybrid genetic algorithms are adopted respectively to train the neural networks in order to compensate coupling effect. Based on analysis of the convergence of the genetic algorithms, the feasibility of the online decoupling algorithm is discussed. The effectiveness of the algorithm has been shown by numerical simulations combing nonlinear MIMO system
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