{"title":"在线谐波辨识的自适应线性学习:附研究案例综述","authors":"P. Wira, Thien-Minh Nguyen","doi":"10.1109/IJCNN.2013.6706970","DOIUrl":null,"url":null,"abstract":"This work reviews Adaline-based techniques for estimating Fourier series. The Adaline, with its linear structure and learning, fits a Fourier series by expressing any periodic signal as a sum of harmonic terms. The learning with elementary harmonic inputs enforces the weights to converge to the amplitudes. The Adaline therefore individually identifies the amplitudes of the harmonic terms present in the measured signal in real-time. Relevant study cases are provided. Performances are evaluated and show that harmonic terms of the signals are efficiently estimated.","PeriodicalId":376975,"journal":{"name":"The 2013 International Joint Conference on Neural Networks (IJCNN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Adaptive linear learning for on-line harmonic identification: An overview with study cases\",\"authors\":\"P. Wira, Thien-Minh Nguyen\",\"doi\":\"10.1109/IJCNN.2013.6706970\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work reviews Adaline-based techniques for estimating Fourier series. The Adaline, with its linear structure and learning, fits a Fourier series by expressing any periodic signal as a sum of harmonic terms. The learning with elementary harmonic inputs enforces the weights to converge to the amplitudes. The Adaline therefore individually identifies the amplitudes of the harmonic terms present in the measured signal in real-time. Relevant study cases are provided. Performances are evaluated and show that harmonic terms of the signals are efficiently estimated.\",\"PeriodicalId\":376975,\"journal\":{\"name\":\"The 2013 International Joint Conference on Neural Networks (IJCNN)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2013 International Joint Conference on Neural Networks (IJCNN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2013.6706970\",\"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 2013 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2013.6706970","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Adaptive linear learning for on-line harmonic identification: An overview with study cases
This work reviews Adaline-based techniques for estimating Fourier series. The Adaline, with its linear structure and learning, fits a Fourier series by expressing any periodic signal as a sum of harmonic terms. The learning with elementary harmonic inputs enforces the weights to converge to the amplitudes. The Adaline therefore individually identifies the amplitudes of the harmonic terms present in the measured signal in real-time. Relevant study cases are provided. Performances are evaluated and show that harmonic terms of the signals are efficiently estimated.
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