Algorithms for Canonical Polyadic Decomposition With Block-Circulant Factors

Eeghem, Frederik Van; Lathauwer, Lieven De

doi:10.1109/lsp.2018.2810109

Cited by 2 publications

(1 citation statement)

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“…Nevertheless, for extending the method to larger-scale problems, the issue of having good initial estimates becomes more relevant. Possibly the work of [17] which focuses on block-circulant structured canonical polyadic decomposition may provide good starting points for initializing our method.…”

Section: Numerical Resultsmentioning

confidence: 99%

Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels

Dreesen

Westwick

Schoukens

et al. 2017

Lecture Notes in Computer Science

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Providing flexibility and user-interpretability in nonlinear system identification can be achieved by means of block-oriented methods. One of such block-oriented system structures is the parallel Wiener-Hammerstein system, which is a sum of Wiener-Hammerstein branches, consisting of static nonlinearities sandwiched between linear dynamical blocks. Parallel Wiener-Hammerstein models have more descriptive power than their single-branch counterparts, but their identification is a non-trivial task that requires tailored system identification methods. In this work, we will tackle the identification problem by performing a tensor decomposition of the Volterra kernels obtained from the nonlinear system. We illustrate how the parallel Wiener-Hammerstein block-structure gives rise to a joint tensor decomposition of the Volterra kernels with block-circulant structured factors. The combination of Volterra kernels and tensor methods is a fruitful way to tackle the parallel Wiener-Hammerstein system identification task. In simulation experiments, we were able to reconstruct very accurately the underlying blocks under noisy conditions.

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Section: Numerical Resultsmentioning

confidence: 99%