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

TL;DR

This paper introduces a tensor decomposition approach of Volterra kernels for identifying parallel Wiener-Hammerstein systems, enhancing interpretability and accuracy in nonlinear system modeling.

Contribution

It presents a novel method using tensor decomposition of Volterra kernels to identify parallel Wiener-Hammerstein systems, addressing a complex system identification challenge.

Findings

Accurately reconstructed system blocks under noisy conditions

Demonstrated effectiveness of tensor methods for nonlinear system identification

Enhanced interpretability of block-oriented models

Abstract

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.