Abstract
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 simplified model is thus able to keep its accuracy as high as possible.
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Acknowledgments
This work was supported in part by the Fund for Scientific Research (FWO-Vlaanderen), the Flemish Government (Methusalem), the Belgian Government through the Interuniversity Poles of Attraction (IAP VII) Program, the ERC advanced grant SNLSID under contract 320378, the ERC starting grant SLRA under contract 258581, and FWO project G028015N.
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Hollander, G., Dreesen, P., Ishteva, M., Schoukens, J. (2017). An Initialization Method for Nonlinear Model Reduction Using the CP Decomposition. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_11
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DOI: https://doi.org/10.1007/978-3-319-53547-0_11
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