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Transient Performance Analysis of the L1-RLS
- Source :
- IEEE Signal Processing Letters, IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, In press, ⟨10.1109/LSP.2021.3127467⟩
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- The recursive least-squares algorithm with $\ell_1$-norm regularization ($\ell_1$-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems. Nevertheless few works have studied its stochastic behavior, in particular its transient performance. In this letter, we derive analytical models of the transient behavior of the $\ell_1$-RLS in the mean and mean-square sense. Simulation results illustrate the accuracy of these models.<br />Comment: 5 pages, 2 figures
- Subjects :
- Signal Processing (eess.SP)
sparse system
Stochastic behavior
Applied Mathematics
Online identification
020206 networking & telecommunications
02 engineering and technology
Sense (electronics)
Transient analysis
Regularization (mathematics)
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Rate of convergence
L1-RLS
Signal Processing
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
Applied mathematics
Transient (oscillation)
Electrical Engineering and Systems Science - Signal Processing
Electrical and Electronic Engineering
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Mathematics
online identification
Subjects
Details
- Language :
- English
- ISSN :
- 10709908
- Database :
- OpenAIRE
- Journal :
- IEEE Signal Processing Letters, IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, In press, ⟨10.1109/LSP.2021.3127467⟩
- Accession number :
- edsair.doi.dedup.....518c552aafe0141983660f53dc157e8c