ZA-APA with zero attractor controller selection criterion for sparse system identification

The zero attraction affine projection algorithm (ZA-APA) is one of the sparse APAs that are based on l 1 -norm penalty function. It provides faster convergence and lower steady-state error than the conventional APA when the system is sparse. Most of the analysis for attraction-type APA is normally b...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2018-02, Vol.12 (2), p.371-377
Main Authors: Radhika, S., Sivabalan, A.
Format: Article
Language:English
Subjects:
Attraction
Computer Imaging
Computer Science
Computer simulation
Convergence
Criteria
Error detection
Image Processing and Computer Vision
Multimedia Information Systems
Original Paper
Pattern Recognition and Graphics
Penalty function
Signal,Image and Speech Processing
Steady state
System identification
Vision
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Summary:The zero attraction affine projection algorithm (ZA-APA) is one of the sparse APAs that are based on l 1 -norm penalty function. It provides faster convergence and lower steady-state error than the conventional APA when the system is sparse. Most of the analysis for attraction-type APA is normally based on white Gaussian assumption for the input. In this paper, a detailed performance analysis of the ZA-APA is performed using individual weight error variance (IWV) method. Using the IWV method, the condition for the convergence in mean and mean square error sense and the steady-state mean square deviation (MSD) error based on non-Gaussian input assumption is derived. Theoretical derivation reveals that the value of zero attractor controller plays a key role in the final steady-state error. Hence, a selection criterion for zero attractor controller based on the steady-state MSD error is proposed. Finally, simulations are performed to validate the analysis made in the context of unknown system identification.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-017-1168-6