{"title":"一类多项式微分系统的参数辨识","authors":"A. Pearson, F. Lee","doi":"10.1109/CDC.1984.272374","DOIUrl":null,"url":null,"abstract":"A least squares parameter identification technique is developed for a class of nonlinear deterministic systems modeled by polynomial input-output differential equations. The basis of the technique is Shinbrot's method of moment functionals using trigonometric modulating functions. Given the input-output data over sequential time intervals, the underlying computations utilize a Fast Fourier Transform algorithm on polynomials of the data without the need for estimating unknown initial conditions at the start of each finite time interval.","PeriodicalId":269680,"journal":{"name":"The 23rd IEEE Conference on Decision and Control","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1984-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Parameter identification for a class of polynomial differential systems\",\"authors\":\"A. Pearson, F. Lee\",\"doi\":\"10.1109/CDC.1984.272374\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A least squares parameter identification technique is developed for a class of nonlinear deterministic systems modeled by polynomial input-output differential equations. The basis of the technique is Shinbrot's method of moment functionals using trigonometric modulating functions. Given the input-output data over sequential time intervals, the underlying computations utilize a Fast Fourier Transform algorithm on polynomials of the data without the need for estimating unknown initial conditions at the start of each finite time interval.\",\"PeriodicalId\":269680,\"journal\":{\"name\":\"The 23rd IEEE Conference on Decision and Control\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 23rd IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1984.272374\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 23rd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1984.272374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter identification for a class of polynomial differential systems
A least squares parameter identification technique is developed for a class of nonlinear deterministic systems modeled by polynomial input-output differential equations. The basis of the technique is Shinbrot's method of moment functionals using trigonometric modulating functions. Given the input-output data over sequential time intervals, the underlying computations utilize a Fast Fourier Transform algorithm on polynomials of the data without the need for estimating unknown initial conditions at the start of each finite time interval.