Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
Signal Processing
2021-09-21 v1
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.
Keywords
Cite
@article{arxiv.2109.09584,
title = {Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems},
author = {Konstantin Usevich and Philippe Dreesen and Mariya Ishteva},
journal= {arXiv preprint arXiv:2109.09584},
year = {2021}
}