Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition
  of Volterra Kernels   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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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th