**Reference:**

Z. Hidayat,
R. Babuska,
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.

Online version of the paper

Corresponding technical report: pdf file (1.15 MB)

@article{HidBab:17-018,

author={Z. Hidayat and R. Babu{\v{s}}ka and A. N{\'{u}}{\~{n}}ez and B. {D}e Schutter},

title={Identification of distributed-parameter systems from sparse measurements},

journal={Applied Mathematical Modelling},

volume={51},

pages={605--625},

month=nov,

year={2017},

doi={10.1016/j.apm.2017.07.001}

}

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