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An initialization method for nonlinear model reduction using the CP decomposition

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

Every parametric model lies on the trade-off line between
accuracy and interpretability. Increasing the interpretability of a model,
while keeping the accuracy as good as possible, is of great importance
for every existing model today. Currently, some nonlinear models in the
field of block-oriented modeling are hard to interpret, and need to be
simplified. Therefore, we designed a model-reduction technique based on
the Canonical Polyadic tensor Decomposition , which can be used for a
special type of static nonlinear multiple-input-multiple-output models.
We analyzed how the quality of the model varies as the model order is
reduced. This paper introduces a special initialization and compares it
with a randomly chosen initialization point.
Using the method based on tensor decompositions ensures smaller errors
than when using the brute-force optimization method. The resulting sim-
plified model is thus able to keep its accuracy as high as possible.
Boek: Latent Variable Analysis and Signal Separation - 13th International Conference, LVA/ICA 2017, Proceedings
Series: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pagina's: 111-120
Aantal pagina's: 10
ISBN: 978-3-319-53546-3
Jaar van publicatie:2017
Trefwoorden:CP decomposition, Model order reduction, Multiple-input-multiple-output model, Tensor decomposition