{"title":"基于数据预滤波的Wiener模型识别","authors":"C. T. Chou, M. Verhaegen","doi":"10.1109/CDC.1999.832850","DOIUrl":null,"url":null,"abstract":"Data pre-filtering, i.e. filtering both the input and output data with an identical scalar filter, is a useful tool in the identification of linear time-invariant systems but is generally not applicable to nonlinear system identification. We show that, if the correlation approach is used to identify Wiener models, which are nonlinear, pre-filtering can be used if the input excitation is Gaussian.","PeriodicalId":137513,"journal":{"name":"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Identification of Wiener models with data pre-filtering\",\"authors\":\"C. T. Chou, M. Verhaegen\",\"doi\":\"10.1109/CDC.1999.832850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data pre-filtering, i.e. filtering both the input and output data with an identical scalar filter, is a useful tool in the identification of linear time-invariant systems but is generally not applicable to nonlinear system identification. We show that, if the correlation approach is used to identify Wiener models, which are nonlinear, pre-filtering can be used if the input excitation is Gaussian.\",\"PeriodicalId\":137513,\"journal\":{\"name\":\"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1999.832850\",\"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 of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1999.832850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Wiener models with data pre-filtering
Data pre-filtering, i.e. filtering both the input and output data with an identical scalar filter, is a useful tool in the identification of linear time-invariant systems but is generally not applicable to nonlinear system identification. We show that, if the correlation approach is used to identify Wiener models, which are nonlinear, pre-filtering can be used if the input excitation is Gaussian.