Abstract
The normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. Sufficient conditions for the NLMF algorithm convergence in the mean are obtained and an analysis of the steady-state performance is carried out with a new approach. The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the NLMF algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 17-31 |
| Number of pages | 15 |
| Journal | Digital Signal Processing: A Review Journal |
| Volume | 17 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2007 |
Bibliographical note
Funding Information:The author acknowledges KFUPM for the support received under fast track grant FT-2004/2.
Keywords
- LMF algorithm
- LMS algorithm
- NLMF algorithm
- NLMS algorithm
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics
- Electrical and Electronic Engineering
- Artificial Intelligence
- Applied Mathematics
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