Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910525704634368 |
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| author | Kuang, Simon Lin, Xinfan |
| author_facet | Kuang, Simon Lin, Xinfan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05382 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems Kuang, Simon Lin, Xinfan Systems and Control Optimization and Control Machine Learning 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. |
| title | Estimation Sample Complexity of a Class of Nonlinear Continuous-time Systems |
| topic | Systems and Control Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2312.05382 |