Non-negative least-mean-square algorithm
Résumé
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.
Mots clés
least mean squares methods
gradient methods
non-negativity
nonnegative constraints
transient analysis
adaptive filtering
parameter estimation
dynamic system modeling
signal processing
nonstationary signal processing
stochastic gradient descent
nonnegativity constraint
Equations
Prediction algorithms
Convergence
Algorithm design and analysis
least mean square algorithms
Facsimile
nonnegative least-mean-square algorithm
Adaptive filters
Mathematical model
adaptive signal processing
system identification
Least squares approximation
| Origine | Fichiers produits par l'(les) auteur(s) |
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