Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems
Not on the map. Why?
Abstract (arXiv)
We present a method of parameter estimation for large class of nonlinear systems, namely those in which the state consists of output derivatives and the flow is linear in the parameter. The method, which solves for the unknown parameter by directly inverting the dynamics using regularized linear regression, is based on new design and analysis ideas for differentiation filtering and regularized least squares. Combined in series, they yield a novel finite-sample bound on mean absolute error of estimation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 46 references that are in the map. First posted on arXiv 8 Dec 2023.
Builds on
5 of 46 references.
References · 5
- 2020Active Learning for Nonlinear System Identification with Guaranteescited by 78
- 2022Statistical Learning Theory for Control: A Finite Sample Perspectivecited by 52
- 2023A Tutorial on the Non-Asymptotic Theory of System Identificationcited by 26
- 2019A Tutorial on Concentration Bounds for System Identificationcited by 19
- 2020Numerical differentiation of noisy data: A unifying multi-objective optimization frameworkcited by 19
Built on by
Not cited yet.