System identification under non-negativity constraints
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.
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]
Origin : Files produced by the author(s)
Loading...