{"title":"Anti-control of chaos based on fuzzy neural networks inverse system method","authors":"H. Ren, Ding-I Liu","doi":"10.1109/ICMLC.2002.1174491","DOIUrl":null,"url":null,"abstract":"The problem considered in the paper is anti-control of chaos for a non-chaotic system via a fuzzy neural network inverse system (FNNIS) method. A Sugeno type fuzzy neural network (FNN) is trained to learn the kinetics of the non-chaotic system. The trained FNN model is employed in the inverse system method, thereby, the exact mathematic model of the system to be controlled is not necessary. The FNN model error upon control is studied and a related theorem is developed. Simulation results for continuous and discrete systems show the effectiveness of the method.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"152 2","pages":"796-799 vol.2"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICMLC.2002.1174491","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1174491","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
The problem considered in the paper is anti-control of chaos for a non-chaotic system via a fuzzy neural network inverse system (FNNIS) method. A Sugeno type fuzzy neural network (FNN) is trained to learn the kinetics of the non-chaotic system. The trained FNN model is employed in the inverse system method, thereby, the exact mathematic model of the system to be controlled is not necessary. The FNN model error upon control is studied and a related theorem is developed. Simulation results for continuous and discrete systems show the effectiveness of the method.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
