کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
561657 | 1451973 | 2010 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal](/preview/png/561657.png)
چکیده انگلیسی
The zero-attracting LMS (ZA-LMS) algorithm is one of the recently published sparse LMS algorithms. It usesan l1-norm penalty in the standard LMS cost function. In this paper, we perform convergence analysis of the ZA-LMS algorithm based on white input signals. The stability condition is examined and the steady-state mean square deviation (MSD) is derived in terms of the system sparsity, system response length, and filter parameters (step size and zero-attractor controller). In addition, we propose a criterion for parameter selection such that the ZA-LMS algorithm outperforms the standard LMS algorithm. The results are demonstrated through computer simulations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 90, Issue 12, December 2010, Pages 3289–3293
Journal: Signal Processing - Volume 90, Issue 12, December 2010, Pages 3289–3293
نویسندگان
Kun Shi, Peng Shi,