On polynomial explicit partial estimator design for nonlinear systems with parametric uncertainties
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909884517187584 |
|---|---|
| author | Alamir, Mazen |
| author_facet | Alamir, Mazen |
| contents | This paper investigates the idea of designing data-driven partial estimators for nonlinear systems showing parametric uncertainties using sparse multivariate polynomial relationships. A general framework is first presented and then validated on two illustrative examples with comparison to different possible Machine/Deep-Learning based alternatives. The results suggests the superiority of the proposed sparse identification scheme, at least when the learning data is small. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On polynomial explicit partial estimator design for nonlinear systems with parametric uncertainties Alamir, Mazen Systems and Control This paper investigates the idea of designing data-driven partial estimators for nonlinear systems showing parametric uncertainties using sparse multivariate polynomial relationships. A general framework is first presented and then validated on two illustrative examples with comparison to different possible Machine/Deep-Learning based alternatives. The results suggests the superiority of the proposed sparse identification scheme, at least when the learning data is small. |
| title | On polynomial explicit partial estimator design for nonlinear systems with parametric uncertainties |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.01638 |