Abstract
The learning speed of an adaptive algorithm can be improved by properly constraining the cost function of the adaptive algorithm. In this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is proposed. The NCLMF algorithm is obtained by constraining the cost function of the standard LMF algorithm to the fourth-order moment of the additive noise. The NCLMF algorithm can be seen as a variable step-size LMF algorithm. The main aim of this work is to derive the NCLMF adaptive algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 136-149 |
| Number of pages | 14 |
| Journal | Signal Processing |
| Volume | 91 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2011 |
Bibliographical note
Funding Information:The authors acknowledge the support of King Fahd University of Petroleum & Minerals for the support provided to carry this research. Also, the authors thank the anonymous reviewers for their constructive suggestions which have helped improve the paper.
Keywords
- Adaptive filters
- Constrained optimization
- LMF
- LMS
- NCLMF algorithm
- Noise constraints
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
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