System identification under non-negativity constraints - Normandie Université Access content directly
Conference Papers Year : 2010

System identification under non-negativity constraints

Cédric Richard
Henri Lantéri


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.
Fichier principal
Vignette du fichier
paper.pdf (400.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02111262 , version 1 (25-04-2019)


  • HAL Id : hal-02111262 , version 1


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⟩
65 View
65 Download


Gmail Facebook X LinkedIn More