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Publication

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

Journal Contribution - Journal Article Conference Contribution

© Springer International Publishing AG 2017. 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 blockstructure 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 WienerHammerstein system identification task. In simulation experiments, we were able to reconstruct very accurately the underlying blocks under noisy conditions.
Journal: Lecture Notes in Computer Science
ISSN: 0302-9743
Volume: 10169
Pages: 16 - 25
Number of pages: 10
Publication year:2017