Nonnegative Least-Mean-Square Algorithm

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estima...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 59; H. 11; S. 5225 - 5235
Hauptverfasser: Jie Chen, Richard, C., Bermudez, J. C. M., Honeine, P.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.11.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive filters
adaptive signal processing
Algorithm design and analysis
Algorithms
Applied sciences
Consistency
Convergence
Descent
Detection, estimation, filtering, equalization, prediction
Dynamical systems
Dynamics
Equations
Estimates
Exact sciences and technology
Facsimile
Information, signal and communications theory
least mean square algorithms
Least squares approximation
Mathematical model
nonnegative constraints
Prediction algorithms
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
transient analysis
Performance evaluation
Parameter estimation
Adaptive filters
Non stationary condition
Adaptive signal processing
Adaptive filter
Dynamical system
Stochastic method
Algorithm
nonnegative constraints
Descent method
Signal processing
least mean square algorithms
System identification
Stationary signal
Dynamic model
Transient analysis
Gradient method
Least mean squares methods
ISSN:1053-587X, 1941-0476
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Beschreibung
Zusammenfassung:Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under nonnegativity constraints. We derive the so-called nonnegative least-mean-square algorithm (NNLMS) based on stochastic gradient descent, and we analyze its convergence. Experiments are conducted to illustrate the performance of this approach and consistency with the analysis.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2011.2162508