System identification under non-negativity constraints
Résumé
Dynamic system modeling plays a crucial role in the development of techniques for stationary and non-stationary signal processing. Due to the inherent physical characteristics of systems usually under investigation, non-negativity is a desired constraint that can be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under non-negativity constraints. We derive additive and multiplicative weight update algorithms, based on (stochastic) gradient descent of mean-square error or Kullback-Leibler divergence. Experiments are conducted to validate the proposed approach.
Domaines
Machine Learning [stat.ML] Traitement du signal et de l'image [eess.SP] Statistiques [math.ST] Traitement du signal et de l'image [eess.SP] Réseau de neurones [cs.NE] Apprentissage [cs.LG] Ordinateur et société [cs.CY] Vision par ordinateur et reconnaissance de formes [cs.CV] Intelligence artificielle [cs.AI]| Origine | Fichiers produits par l'(les) auteur(s) |
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