{"title":"正弦信号的自适应参数辨识","authors":"J. Na, Juan Yang, Xing Wu, Yu Guo","doi":"10.3182/20130902-3-CN-3020.00096","DOIUrl":null,"url":null,"abstract":"Abstract A novel adaptive identification framework is proposed for sinusoidal signals to estimate all unknown parameters (i.e. offset, amplitude, frequency and phase). The proposed identification is independent of any observer/predictor design and thus can be implemented in a simplified manner. The adaptive laws are driven by appropriate parameter error information derived by applying filter operations on the output measurements. Globally exponential convergence of the parameter estimation is proved. The proposed idea is further extended for multi-sinusoid signals and verified in terms of simulations.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Adaptive Parameter Identification of Sinusoidal Signals\",\"authors\":\"J. Na, Juan Yang, Xing Wu, Yu Guo\",\"doi\":\"10.3182/20130902-3-CN-3020.00096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A novel adaptive identification framework is proposed for sinusoidal signals to estimate all unknown parameters (i.e. offset, amplitude, frequency and phase). The proposed identification is independent of any observer/predictor design and thus can be implemented in a simplified manner. The adaptive laws are driven by appropriate parameter error information derived by applying filter operations on the output measurements. Globally exponential convergence of the parameter estimation is proved. The proposed idea is further extended for multi-sinusoid signals and verified in terms of simulations.\",\"PeriodicalId\":90521,\"journal\":{\"name\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3182/20130902-3-CN-3020.00096\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3182/20130902-3-CN-3020.00096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive Parameter Identification of Sinusoidal Signals
Abstract A novel adaptive identification framework is proposed for sinusoidal signals to estimate all unknown parameters (i.e. offset, amplitude, frequency and phase). The proposed identification is independent of any observer/predictor design and thus can be implemented in a simplified manner. The adaptive laws are driven by appropriate parameter error information derived by applying filter operations on the output measurements. Globally exponential convergence of the parameter estimation is proved. The proposed idea is further extended for multi-sinusoid signals and verified in terms of simulations.