Reference
Z. Hidayat, R. Babuška, A. Núñez, and B. De Schutter,
"Identification of distributed-parameter systems from sparse measurements,"
Applied Mathematical Modelling, vol. 51, pp. 605-625,
Nov. 2017.
Abstract
In this paper, a methodology for the identification of distributed-parameter
systems is proposed, based on finite-difference discretization on a grid in
space and time. It is considered the case when the partial differential
equation describing the system is not known. The sensor locations are given and
fixed, but not all grid points contain sensors. Per grid point, a model is
constructed by means of lumped-parameter system identification, using
measurements at neighboring grid points as inputs. As the resulting model might
become overly complex due to the involvement of neighboring measurements along
with their time lags, the Lasso method is used to select the most relevant
measurements and so to simplify the model. Two examples are reported to
illustrate the effectiveness of the methodology, a simulated two-dimensional
heat conduction process and the construction of a greenhouse climate model from
real measurements.
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BibTeX
@article{HidBab:17-018,
author = {Hidayat, Zul and Babu{\v{s}}ka, Robert and N{\'{u}}{\~{n}}ez,
Alfredo and De Schutter, Bart},
title = {Identification of Distributed-Parameter Systems from Sparse
Measurements},
journal = {Applied Mathematical Modelling},
volume = {51},
pages = {605--625},
month = nov,
year = {2017}
}