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Communication Dans Un Congrès Année : 2013

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.
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Dates et versions

hal-01966010 , version 1 (27-12-2018)

Identifiants

  • HAL Id : hal-01966010 , version 1

Citer

Jie Chen, Cédric Richard, José C. M. Bermudez, Paul Honeine. Non-stationary Analysis of the Convergence of the Non-negative Least-mean-square Algorithm. Proc. 21th European Conference on Signal Processing (EUSIPCO), 2013, Marrakech, Morocco. pp.1-5. ⟨hal-01966010⟩
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