Reference
Z. Hidayat, A. Núñez, R. Babuška, and B. De Schutter,
"Identification of distributed-parameter systems with missing data,"
Proceedings of the 2012 IEEE International Conference on Control
Applications, Dubrovnik, Croatia, pp. 1014-1019, Oct. 2012.
Abstract
In this paper we address the identification of linear distributed-parameter
systems with missing data. This setting is relevant in, for instance, sensor
networks, where data are frequently lost due to transmission errors. We
consider an identification problem where the only information available about
the system are the input-output measurements from a set of sensors placed at
known fixed locations in the distributed-parameter system. The model is
represented as a set of coupled multi-input, single-output autoregressive with
exogenous input (ARX) submodels. Total least-squares estimation is employed to
obtain an unbiased parameter estimate in the presence of sensor noise. The
missing samples are reconstructed with the help of an iterative algorithm. To
approximate the value of the variables of interest in locations with no
sensors, we use cubic B-splines to preserve the continuity of the first-order
and second-order spatial derivatives. The method is applied to a simulated
one-dimensional heat-conduction process.
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BibTeX
@inproceedings{HidDeS:12-023,
author = {Hidayat, Zul and N{\'{u}}{\~{n}}ez, Alfredo and Babu{\v{s}}ka,
Robert and De Schutter, Bart},
title = {Identification of Distributed-Parameter Systems with Missing
Data},
booktitle = {Proceedings of the 2012 IEEE International Conference on
Control Applications},
address = {Dubrovnik, Croatia},
pages = {1014--1019},
month = oct,
year = {2012}
}