Transient performance degradation of the LMS, RLS, sign, signed regressor, and sign-sign algorithms with data correlation

The transient performance degradation, for correlated input data, is studied for various adaptive algorithms. The algorithms are least mean square (LMS), recursive least squares (RLS), sign (SA), signed regressor (SRA), and sign-sign (SSA). Analysis is performed for adaptive plant identification wit...

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Vydané v:IEEE transactions on circuits and systems. 2, Analog and digital signal processing Ročník 46; číslo 8; s. 1055 - 1062
Hlavný autor: Eweda, E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.08.1999
Institute of Electrical and Electronics Engineers
Predmet:
Adaptive algorithm
Analytical models
Applied sciences
Circuit properties
Closed-form solution
Convergence
Degradation
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Frequency filters
Least squares approximation
Least squares methods
Performance analysis
Resonance light scattering
Time measurement
Adaptive algorithm
Computer simulation
Least squares method
Adaptive filtering
Convergence rate
Signal processing
Identification
Signed regressor
Covariance matrix
Data correlation
ISSN:1057-7130
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Popis
Shrnutí:The transient performance degradation, for correlated input data, is studied for various adaptive algorithms. The algorithms are least mean square (LMS), recursive least squares (RLS), sign (SA), signed regressor (SRA), and sign-sign (SSA). Analysis is performed for adaptive plant identification with stationary Gaussian inputs. A closed-form expression for the mean convergence time is derived for each algorithm. The degradation measure used is the ratio of convergence time for correlated data to the convergence time for white data. The LMS and SRA degradations are the same. The SA and SSA degradations are also the same. The smallest degradation occurs for RLS, the largest for LMS and SRA. The SRA, RLS, and LMS degradations are independent of plant-noise variance. The SA and SSA degradations increase with increased noise variance. The LMS and SRA degradations do not depend upon the weight initialization, RLS (SA and SSA) depends weakly (significantly) upon the weight initialization. The analytical results are supported by simulations.
Bibliografia:ObjectType-Article-2
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content type line 23
ISSN:1057-7130
DOI:10.1109/82.782049