On polynomial explicit partial estimator design for nonlinear systems with parametric uncertainties
By: Mazen Alamir
Potential Business Impact:
Finds patterns in messy data with less information.
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.
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