{"title":"自适应滤波算法","authors":"S. Theodoridis","doi":"10.1109/IMTC.2001.929455","DOIUrl":null,"url":null,"abstract":"System identification (SI) is the task of specifying an unknown system's model in terms of the available experimental evidence, that is a set of input-desired output response signal samples. System identification is a central issue in a large number of application areas, such as control, channel equalization, echo cancellation. This state-of-the-art article focuses on systems that can be modeled in terms of a Finite Impulse Response (FIR) and its goal is to present the available palette of adaptive SI algorithms in a unifying way.","PeriodicalId":68878,"journal":{"name":"Journal of Measurement Science and Instrumentation","volume":"9 1","pages":"1497-1501 vol.3"},"PeriodicalIF":0.0000,"publicationDate":"2001-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Adaptive filtering algorithms\",\"authors\":\"S. Theodoridis\",\"doi\":\"10.1109/IMTC.2001.929455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"System identification (SI) is the task of specifying an unknown system's model in terms of the available experimental evidence, that is a set of input-desired output response signal samples. System identification is a central issue in a large number of application areas, such as control, channel equalization, echo cancellation. This state-of-the-art article focuses on systems that can be modeled in terms of a Finite Impulse Response (FIR) and its goal is to present the available palette of adaptive SI algorithms in a unifying way.\",\"PeriodicalId\":68878,\"journal\":{\"name\":\"Journal of Measurement Science and Instrumentation\",\"volume\":\"9 1\",\"pages\":\"1497-1501 vol.3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Measurement Science and Instrumentation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMTC.2001.929455\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Measurement Science and Instrumentation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMTC.2001.929455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
System identification (SI) is the task of specifying an unknown system's model in terms of the available experimental evidence, that is a set of input-desired output response signal samples. System identification is a central issue in a large number of application areas, such as control, channel equalization, echo cancellation. This state-of-the-art article focuses on systems that can be modeled in terms of a Finite Impulse Response (FIR) and its goal is to present the available palette of adaptive SI algorithms in a unifying way.