Novel Sparse Algorithms based on Lyapunov Stability for Adaptive System Identification
Loading...
Date
2018-04
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Společnost pro radioelektronické inženýrství
Altmetrics
Abstract
Adaptive filters are extensively used in the identification of an unknown system. Unlike several gradient-search based adaptive filtering techniques, the Lyapunov Theory-based Adaptive Filter offers improved convergence and stability. When the system is described by a sparse model, the performance of Lyapunov Adaptive (LA) filter is degraded since it fails to exploit the system sparsity. In this paper, the Zero-Attracting Lyapunov Adaptation algorithm (ZA-LA), the Reweighted Zero-Attracting Lyapunov Adaptation algorithm (RZA-LA) and an affine combination scheme of the LA and proposed ZA-LA filters are proposed. The ZA-LA algorithm is based on ℓ1-norm relaxation while the RZA-LA algorithm uses a log-sum penalty to accelerate convergence when identifying sparse systems. It is shown by simulations that the proposed algorithms can achieve better convergence than the existing LMS/LA filter for a sparse system, while the affine combination scheme is robust in identifying systems with variable sparsity.
Description
Citation
Radioengineering. 2018 vol. 27, č. 1, s. 270-280. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2018/18_01_0270_0280.pdf
https://www.radioeng.cz/fulltexts/2018/18_01_0270_0280.pdf
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en