Canonical block-oriented model
Andrew Polar, Michael Poluektov

TL;DR
This paper introduces a unified approach to modeling block-oriented systems by replacing complex configurations with a single Urysohn block and static non-linearity, along with a non-parametric identification method.
Contribution
It demonstrates that all block-oriented models can be simplified to a Urysohn-based structure and provides a new non-parametric identification technique.
Findings
Any block-oriented model can be replaced by a Urysohn and static non-linear block.
A non-parametric identification method for this model is proposed.
The approach simplifies modeling and identification of complex systems.
Abstract
The block-oriented models are usually based on linear dynamic and non-linear static blocks that are connected in various sequential/parallel ways. Some particular configurations of the involved blocks result in the well-known Hammerstein, Wiener, Hammerstein-Wiener and generalised Hammerstein models. The Urysohn model is a lesser-known model; it is represented by a single non-linear dynamic block and can be approximated by a number of parallel Hammerstein blocks. In this paper, it is shown that any block-oriented model can be adequately replaced by a single Urysohn block followed by a single static non-linear block. Furthermore, a method of the so-called non-parametric identification of such object is introduced.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsControl Systems and Identification · Fault Detection and Control Systems · Neural Networks and Applications
