{"title":"扩展RLS晶格自适应滤波器变体:误差反馈、归一化和基于数组的算法","authors":"R. Merched","doi":"10.1109/ICNNSP.2003.1279402","DOIUrl":null,"url":null,"abstract":"This paper develops several lattice structures for RLS orthonormally-based input data structures, including error feedback, normalized and array-based forms. All recursions are theoretically equivalent, however they tend to differ in performance under finite precision effects. As a result, we verify that compared to the standard extended lattice equations, the new variants do not improve robustness to quantization, unlike what is normally expected for FlR models.","PeriodicalId":336216,"journal":{"name":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On extended RLS lattice adaptive filter variants: error-feedback, normalized and array-based algorithms\",\"authors\":\"R. Merched\",\"doi\":\"10.1109/ICNNSP.2003.1279402\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper develops several lattice structures for RLS orthonormally-based input data structures, including error feedback, normalized and array-based forms. All recursions are theoretically equivalent, however they tend to differ in performance under finite precision effects. As a result, we verify that compared to the standard extended lattice equations, the new variants do not improve robustness to quantization, unlike what is normally expected for FlR models.\",\"PeriodicalId\":336216,\"journal\":{\"name\":\"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNNSP.2003.1279402\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNNSP.2003.1279402","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On extended RLS lattice adaptive filter variants: error-feedback, normalized and array-based algorithms
This paper develops several lattice structures for RLS orthonormally-based input data structures, including error feedback, normalized and array-based forms. All recursions are theoretically equivalent, however they tend to differ in performance under finite precision effects. As a result, we verify that compared to the standard extended lattice equations, the new variants do not improve robustness to quantization, unlike what is normally expected for FlR models.