{"title":"梯形最陡下降法及其在自适应滤波中的应用","authors":"T. Moir","doi":"10.2174/1876825301003010001","DOIUrl":null,"url":null,"abstract":"The method of steepest-descent is re-visited in continuous time. It is shown that the continuous time version is a vector differential equation the solution of which is found by integration. Since numerical integration has many forms, we show an alternative to the conventional solution by using a Trapezoidal integration solution. This in turn gives a slightly modified least-mean squares (LMS) algorithm. Keyword: Steepest-Descent, Least-mean squares (LMS), Adaptive filters.","PeriodicalId":147157,"journal":{"name":"The Open Signal Processing Journal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The trapezoidal method of steepest-descent and its application to adaptive filtering\",\"authors\":\"T. Moir\",\"doi\":\"10.2174/1876825301003010001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The method of steepest-descent is re-visited in continuous time. It is shown that the continuous time version is a vector differential equation the solution of which is found by integration. Since numerical integration has many forms, we show an alternative to the conventional solution by using a Trapezoidal integration solution. This in turn gives a slightly modified least-mean squares (LMS) algorithm. Keyword: Steepest-Descent, Least-mean squares (LMS), Adaptive filters.\",\"PeriodicalId\":147157,\"journal\":{\"name\":\"The Open Signal Processing Journal\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Open Signal Processing Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1876825301003010001\",\"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 Open Signal Processing Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1876825301003010001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The trapezoidal method of steepest-descent and its application to adaptive filtering
The method of steepest-descent is re-visited in continuous time. It is shown that the continuous time version is a vector differential equation the solution of which is found by integration. Since numerical integration has many forms, we show an alternative to the conventional solution by using a Trapezoidal integration solution. This in turn gives a slightly modified least-mean squares (LMS) algorithm. Keyword: Steepest-Descent, Least-mean squares (LMS), Adaptive filters.