Paper Info
Title
Adaptive Forgetting Factor Recursive Least Squares Adaptive Threshold Nonlinear Algorithm (Aff-Rls-Atna) For Identification Of Nonstationary Systems
Abstract
The recursive least squares (RLS) adaptive algorithm is combined with the "adaptive threshold nonlinear algorithm" (ATNA) proposed by the author, to derive RLS-ATNA, resulting in improvement of the convergence rate of the ATNA that offers robust adaptive filters in impulse noise environments. For application of the RLS-ATNA to identification of random-walk modeled nonstationary systems, an adaptive forgetting factor (AFF) control algorithm is proposed that further improves the tracking performance in the steady state. Through analysis and experiments, the effectiveness of the AFF-RLS-ATNA is demonstrated. Fairly good agreement between the simulation and the theoretically calculated convergence validates the analysis.
Year
DOI
Venue
2003
10.1109/ICASSP.2003.1201757
2003 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL VI, PROCEEDINGS: SIGNAL PROCESSING THEORY AND METHODS
Keywords
Field
DocType
adaptive filter,random walk,adaptive systems,nonlinear systems,convergence,adaptive thresholding,adaptive control,convergence rate,impulse noise,burst noise,steady state,recursive least squares,identification,tracking,adaptive filters
Convergence (routing),Mathematical optimization,Adaptive system,Control theory,Computer science,Adaptive filter,Impulse noise,Rate of convergence,Adaptive algorithm,Adaptive control,Recursive least squares filter
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
1
Name
Order
Citations
PageRank
Shin'ichi Koike1174.93