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arXiv · 2109.09584

Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

Konstantin Usevich·Philippe Dreesen·Mariya Ishteva
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Abstract

We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the case of parallel Wiener-Hammerstein systems, such methods require nontrivial constraints on the factors of the decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for the Jacobian of the nonlinear map from past inputs to the output, we can show that the Jacobian matrix becomes a linear projection of a tensor whose rank is equal to the number of branches. This representation allows us to solve the identification problem as a tensor recovery problem.

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Konstantin Usevich, Philippe Dreesen, Mariya Ishteva. 2021-09-20. Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems. https://arxiv.org/abs/2109.09584

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