Low-Rank Tensor Decompositions for Nonlinear System Identification

A Tutorial with Examples

Journal Article (2022)
Author(s)

K. Batselier (TU Delft - Team Kim Batselier)

Research Group
Team Kim Batselier
Copyright
© 2022 K. Batselier
DOI related publication
https://doi.org/10.1109/MCS.2021.3122268
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 K. Batselier
Research Group
Team Kim Batselier
Issue number
1
Volume number
42
Pages (from-to)
54-74
Reuse Rights

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Abstract

Nonlinear parametric system identification is the estimation of nonlinear models of dynamical systems from measured data. Nonlinear models are parameterized, and it is exactly these parameters that must be estimated. Extending familiar linear models to their nonlinear counterparts quickly leads to practical problems. For example, the generalization of a multivariate linear function to a multivariate polynomial implies that the number of parameters grows exponentially with the total degree of the polynomial. This exponential explosion of model parameters is an instance of the so-called curse of dimensionality. Both the storage and computational complexities are limiting factors in the development of system identification methods for such models.

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