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
Online-Zugang:	Volltext
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