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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Vydané v:	IEEE transactions on signal processing Ročník 59; číslo 11; s. 5225 - 5235
Hlavní autori:	Jie Chen, Richard, C., Bermudez, J. C. M., Honeine, P.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.11.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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Abstract	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.
AbstractList	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.
Author	Jie Chen Richard, C. Honeine, P. Bermudez, J. C. M.
Author_xml	– sequence: 1 surname: Jie Chen fullname: Jie Chen email: chenjieg@sina.com organization: CNRS, Univ. of Technol. of Troyes, Troyes, France – sequence: 2 givenname: C. surname: Richard fullname: Richard, C. email: cedric.richard@unice.fr organization: CNRS, Univ. of Nice Sophia-Antipolis, Nice, France – sequence: 3 givenname: J. C. M. surname: Bermudez fullname: Bermudez, J. C. M. email: j.bermudez@ieee.org organization: Dept. of Electr. Eng., Fed. Univ. of Santa Catarina, Florianopolis, Brazil – sequence: 4 givenname: P. surname: Honeine fullname: Honeine, P. email: paul.honeine@utt.fr organization: CNRS, Univ. of Technol. of Troyes, Troyes, France
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SubjectTerms	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
Title	Nonnegative Least-Mean-Square Algorithm
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