Non-stationary Analysis of the Convergence of the Non-negative Least-mean-square Algorithm
Résumé
Non-negativity is a widely used constraint in parameter estimation procedures due to physical characteristics of systems under investigation. In this paper, we consider an LMS-type algorithm for system identification subject to non-negativity constraints, called Non-Negative Least-Mean-Square algorithm, and its normalized variant. An important contribution of this paper is that we study the stochastic behavior of these algorithms in a non-stationary environment, where the unconstrained solution is characterized by a time-variant mean and is affected by random perturbations. Convergence analysis of these algorithms in a stationary environment can be viewed as a particular case of the convergence model derived in this paper. Simulation results are presented to illustrate the performance of the algorithm and the accuracy of the derived models.
Mots clés
machine learning
convergence of numerical methods
non-stationarity
convergence analysis
time variant mean
unconstrained solution
stochastic behavior
system identification
non-stationary signal
adaptive filtering
Non-negativity constraint
Steady-state
Abstracts
least mean squares methods
parameter estimation
stochastic processes
time-varying systems
nonstationary analysis
nonnegative least mean square algorithm
parameter estimation procedures
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