Abstract
The paper presents an algorithm for the identification of PieceWise Affine Output-Error (PWA-OE) models, which involves the estimation of the parameters defining affine submodels as well as a partition of the regressor space. For the estimation of affine submodel parameters, a bias-correction scheme is presented to correct the bias in the least squares estimates which is caused by the output-error noise structure. The obtained bias-corrected estimates are proven to be consistent under suitable assumptions. The bias-correction method is then combined with a recursive estimation algorithm for clustering the regressors. These clusters are used to compute a partition of the regressor space by employing linear multicategory discrimination. The effectiveness of the proposed methodology is demonstrated via a simulation case study.
| Original language | English |
|---|---|
| Pages (from-to) | 1096-1101 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 53 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 Conference number: 21 https://www.ifac2020.org/ |
Bibliographical note
Funding Information:This work was partially supported by the European H2020-CS2 project ADMITTED, Grant agreement no. GA832003.
Funding
This work was partially supported by the European H2020-CS2 project ADMITTED, Grant agreement no. GA832003.
Keywords
- Bias corrected least-squares
- Hybrid systems
- Output-error models
- PWA regression