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

@article{Usevich2021LowrankTR,
  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={IFAC-PapersOnLine},
  year={2021}
}
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… 
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