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System identification under non-negativity constraints

Jie Chen 1 Cédric Richard 2 Paul Honeine 1 Henri Lantéri 2 Céline Theys 2 
1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
ICD - Institut Charles Delaunay
2 LAGRANGE - Joseph Louis LAGRANGE
Abstract : 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.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02111262
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  • HAL Id : hal-02111262, version 1

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Jie Chen, Cédric Richard, Paul Honeine, Henri Lantéri, Céline Theys. System identification under non-negativity constraints. European Signal Processing Conference, Aug 2010, Aalborg, Denmark. pp.1728-1732. ⟨hal-02111262⟩

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