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]
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