{"title":"一类非线性系统基于稳定性的神经网络控制方法","authors":"E. Tzirkel-Hancock, F. Fallside","doi":"10.1109/IJCNN.1991.170535","DOIUrl":null,"url":null,"abstract":"A direct control scheme for a class of continuous-time nonlinear systems using neural networks is presented. The objective of the control is to track a desired reference signal. This objective is achieved through input/output linearization of the system with neural networks. Learning, based on a stability type algorithm, takes place simultaneously with control. As such, the method is closely related to adaptive control methods and the field of neural network training. In particular, the importance of the property of persistent excitation and its implications for learning with networks of localized receptive fields are discussed.<<ETX>>","PeriodicalId":211135,"journal":{"name":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1991-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A stability based neural network control method for a class of nonlinear systems\",\"authors\":\"E. Tzirkel-Hancock, F. Fallside\",\"doi\":\"10.1109/IJCNN.1991.170535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A direct control scheme for a class of continuous-time nonlinear systems using neural networks is presented. The objective of the control is to track a desired reference signal. This objective is achieved through input/output linearization of the system with neural networks. Learning, based on a stability type algorithm, takes place simultaneously with control. As such, the method is closely related to adaptive control methods and the field of neural network training. In particular, the importance of the property of persistent excitation and its implications for learning with networks of localized receptive fields are discussed.<<ETX>>\",\"PeriodicalId\":211135,\"journal\":{\"name\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.1991.170535\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] 1991 IEEE International Joint Conference on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.1991.170535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A stability based neural network control method for a class of nonlinear systems
A direct control scheme for a class of continuous-time nonlinear systems using neural networks is presented. The objective of the control is to track a desired reference signal. This objective is achieved through input/output linearization of the system with neural networks. Learning, based on a stability type algorithm, takes place simultaneously with control. As such, the method is closely related to adaptive control methods and the field of neural network training. In particular, the importance of the property of persistent excitation and its implications for learning with networks of localized receptive fields are discussed.<>