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System Identification Under Non-Negativity Constraints

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type 01 Jan 2010 France Publisher:Zenodo
Authors: Chen, Jie; Richard, Cédric; Honeine, Paul; Lantéri, Henri; Theys, Céline;

doi: 10.5281/zenodo.42223

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.

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[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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