English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Providing flexibility and user-interpretability in nonlinear systemidentification can be achieved by means of block-oriented methods. One of suchblock-oriented system structures is the parallel Wiener-Hammerstein system,which is a sum of Wiener-Hammerstein branches, consisting of staticnonlinearities sandwiched between linear dynamical blocks. ParallelWiener-Hammerstein models have more descriptive power than their single-branchcounterparts, but their identification is a non-trivial task that requirestailored system identification methods. In this work, we will tackle theidentification problem by performing a tensor decomposition of the Volterrakernels obtained from the nonlinear system. We illustrate how the parallelWiener-Hammerstein block-structure gives rise to a joint tensor decompositionof the Volterra kernels with block-circulant structured factors. Thecombination of Volterra kernels and tensor methods is a fruitful way to tacklethe parallel Wiener-Hammerstein system identification task. In simulationexperiments, we were able to reconstruct very accurately the underlying blocksunder noisy conditions.

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