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
2 LAGRANGE - Joseph Louis LAGRANGE

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.

Domains: Statistics [stat]; Machine Learning [stat.ML]; Engineering Sciences [physics]; Signal and Image processing; Mathematics [math]; Statistics [math.ST]; Computer Science [cs]; Signal and Image Processing; Computer Science [cs]; Neural and Evolutionary Computing [cs.NE]; Computer Science [cs]; Machine Learning [cs.LG]; Computer Science [cs]; Computers and Society [cs.CY]; Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]; Computer Science [cs]; Artificial Intelligence [cs.AI]

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⟩