{"title":"IIR进化数字滤波器与其他自适应数字滤波器在多峰曲面上的收敛性比较","authors":"M. Abe, M. Kawamata","doi":"10.1109/ACSSC.1997.679187","DOIUrl":null,"url":null,"abstract":"This paper demonstrates a comparison of the convergence behavior of the IIR evolutionary digital filter (IIR-EDF), the LMS adaptive digital filter (LMS-ADF) and the adaptive digital filter based on the simple genetic algorithm (SGA-ADF) on a multiple-peak surface. In numerical examples, the authors use a reduced-order system identification to simulate a multiple-peak surface in which local minimum problems can be encountered. The experimental results show that the EDF adaptive algorithm can search the global minimum in the multiple-peak surface of these examples and has a smaller adaptation noise than the other algorithms.","PeriodicalId":240431,"journal":{"name":"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Comparison of the convergence of IIR evolutionary digital filters and other adaptive digital filters on a multiple-peak surface\",\"authors\":\"M. Abe, M. Kawamata\",\"doi\":\"10.1109/ACSSC.1997.679187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper demonstrates a comparison of the convergence behavior of the IIR evolutionary digital filter (IIR-EDF), the LMS adaptive digital filter (LMS-ADF) and the adaptive digital filter based on the simple genetic algorithm (SGA-ADF) on a multiple-peak surface. In numerical examples, the authors use a reduced-order system identification to simulate a multiple-peak surface in which local minimum problems can be encountered. The experimental results show that the EDF adaptive algorithm can search the global minimum in the multiple-peak surface of these examples and has a smaller adaptation noise than the other algorithms.\",\"PeriodicalId\":240431,\"journal\":{\"name\":\"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.1997.679187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.1997.679187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of the convergence of IIR evolutionary digital filters and other adaptive digital filters on a multiple-peak surface
This paper demonstrates a comparison of the convergence behavior of the IIR evolutionary digital filter (IIR-EDF), the LMS adaptive digital filter (LMS-ADF) and the adaptive digital filter based on the simple genetic algorithm (SGA-ADF) on a multiple-peak surface. In numerical examples, the authors use a reduced-order system identification to simulate a multiple-peak surface in which local minimum problems can be encountered. The experimental results show that the EDF adaptive algorithm can search the global minimum in the multiple-peak surface of these examples and has a smaller adaptation noise than the other algorithms.