Stable nonlinear identification from noisy repeated experiments via convex optimization
This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this class when a small set of repeated experiments with suitably independent measure...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2014
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| Online Access: | http://hdl.handle.net/1721.1/90399 https://orcid.org/0000-0001-9088-0205 |
| _version_ | 1826200910797209600 |
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| author | Tobenkin, Mark M. Manchester, Ian R. Megretski, Alexandre |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tobenkin, Mark M. Manchester, Ian R. Megretski, Alexandre |
| author_sort | Tobenkin, Mark M. |
| collection | MIT |
| description | This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this class when a small set of repeated experiments with suitably independent measurement noise is available. Stability of the estimated models is guaranteed without any assumptions on the input-output data. We first present a convex optimization scheme for identifying stable state-space models from empirical moments. Next, we provide a method for using repeated experiments to remove the effect of noise on these moment and model estimates. The technique is demonstrated on a simple simulated example. |
| first_indexed | 2024-09-23T11:43:38Z |
| format | Article |
| id | mit-1721.1/90399 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:43:38Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/903992022-10-01T05:32:22Z Stable nonlinear identification from noisy repeated experiments via convex optimization Tobenkin, Mark M. Manchester, Ian R. Megretski, Alexandre Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tobenkin, Mark M. Megretski, Alexandre This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this class when a small set of repeated experiments with suitably independent measurement noise is available. Stability of the estimated models is guaranteed without any assumptions on the input-output data. We first present a convex optimization scheme for identifying stable state-space models from empirical moments. Next, we provide a method for using repeated experiments to remove the effect of noise on these moment and model estimates. The technique is demonstrated on a simple simulated example. 2014-09-26T16:09:20Z 2014-09-26T16:09:20Z 2013-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-0178-4 978-1-4799-0177-7 978-1-4799-0175-3 0743-1619 http://hdl.handle.net/1721.1/90399 Tobenkin, Mark M., Ian R. Manchester, and Alexandre Megretski. “Stable Nonlinear Identification from Noisy Repeated Experiments via Convex Optimization.” 2013 American Control Conference (June 2013). https://orcid.org/0000-0001-9088-0205 en_US http://dx.doi.org/10.1109/ACC.2013.6580441 Proceedings of the 2013 American Control Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Tobenkin, Mark M. Manchester, Ian R. Megretski, Alexandre Stable nonlinear identification from noisy repeated experiments via convex optimization |
| title | Stable nonlinear identification from noisy repeated experiments via convex optimization |
| title_full | Stable nonlinear identification from noisy repeated experiments via convex optimization |
| title_fullStr | Stable nonlinear identification from noisy repeated experiments via convex optimization |
| title_full_unstemmed | Stable nonlinear identification from noisy repeated experiments via convex optimization |
| title_short | Stable nonlinear identification from noisy repeated experiments via convex optimization |
| title_sort | stable nonlinear identification from noisy repeated experiments via convex optimization |
| url | http://hdl.handle.net/1721.1/90399 https://orcid.org/0000-0001-9088-0205 |
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