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Conference papers
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.
Cited literature [9 references]
https://hal-normandie-univ.archives-ouvertes.fr/hal-02111262
Contributor : Paul Honeine <>
Submitted on : Thursday, April 25, 2019 - 11:01:46 PM
Last modification on : Wednesday, October 14, 2020 - 4:23:39 AM
Contributor : Paul Honeine <>
Submitted on : Thursday, April 25, 2019 - 11:01:46 PM
Last modification on : Wednesday, October 14, 2020 - 4:23:39 AM
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