{"title":"非线性系统分段仿射系统辨识的设计考虑","authors":"Niel Canty, T. O'Mahony","doi":"10.1109/MED.2009.5164532","DOIUrl":null,"url":null,"abstract":"A PieceWise AutoRegressive eXogenous (PWARX) model for the AMIRA DR300 DC motor is identified using clustering techniques available in the Hybrid Identification Toolbox (HIT). The choice of design parameters like magnitude of the noise variance and size of the local dataset are discussed. These parameters influence the quality and performance of the PWARX model. The performance of the PWARX model is compared with that of a linear model. The results show superior performance of the PWARX model especially in the nonlinear regions.","PeriodicalId":422386,"journal":{"name":"2009 17th Mediterranean Conference on Control and Automation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Design considerations for piecewise affine system identification of nonlinear systems\",\"authors\":\"Niel Canty, T. O'Mahony\",\"doi\":\"10.1109/MED.2009.5164532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A PieceWise AutoRegressive eXogenous (PWARX) model for the AMIRA DR300 DC motor is identified using clustering techniques available in the Hybrid Identification Toolbox (HIT). The choice of design parameters like magnitude of the noise variance and size of the local dataset are discussed. These parameters influence the quality and performance of the PWARX model. The performance of the PWARX model is compared with that of a linear model. The results show superior performance of the PWARX model especially in the nonlinear regions.\",\"PeriodicalId\":422386,\"journal\":{\"name\":\"2009 17th Mediterranean Conference on Control and Automation\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 17th Mediterranean Conference on Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MED.2009.5164532\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 17th Mediterranean Conference on Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MED.2009.5164532","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design considerations for piecewise affine system identification of nonlinear systems
A PieceWise AutoRegressive eXogenous (PWARX) model for the AMIRA DR300 DC motor is identified using clustering techniques available in the Hybrid Identification Toolbox (HIT). The choice of design parameters like magnitude of the noise variance and size of the local dataset are discussed. These parameters influence the quality and performance of the PWARX model. The performance of the PWARX model is compared with that of a linear model. The results show superior performance of the PWARX model especially in the nonlinear regions.
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