Convergence analysis of kernel LMS algorithm with pre-tuned dictionary

Jie Chen, Wei Gao, Cedric Richard, Jose Carlos M. Bermudez

科研成果: 书/报告/会议事项章节会议稿件同行评审

30 引用 (Scopus)

摘要

The kernel least-mean-square (KLMS) algorithm is an appealing tool for online identification of nonlinear systems due to its simplicity and robustness. In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a finite-order model. This dictionary has a significant impact on performance, and requires careful consideration. Theoretical analysis of KLMS as a function of dictionary setting has rarely, if ever, been addressed in the literature. In an analysis previously published by the authors, the dictionary elements were assumed to be governed by the same probability density function of the input data. In this paper, we modify this study by considering the dictionary as part of the filter parameters to be set. This theoretical analysis paves the way for future investigations on KLMS dictionary design.

源语言英语
主期刊名2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
出版商Institute of Electrical and Electronics Engineers Inc.
7243-7247
页数5
ISBN(印刷版)9781479928927
DOI
出版状态已出版 - 2014
已对外发布
活动2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, 意大利
期限: 4 5月 20149 5月 2014

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

会议

会议2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
国家/地区意大利
Florence
时期4/05/149/05/14

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