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

R. Merched
{"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}
引用次数: 0

摘要

本文开发了几种基于RLS正交输入数据结构的格结构,包括误差反馈、归一化和基于数组的形式。所有递归在理论上都是等价的,但是在有限精度效果下,它们的性能往往不同。因此,我们验证了与标准扩展晶格方程相比,新的变体并没有提高量化的鲁棒性,这与FlR模型通常期望的不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信