Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914709097152512 |
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| author | Abdalmoaty, Mohamed Balta, Efe C. Lygeros, John Smith, Roy S. |
| author_facet | Abdalmoaty, Mohamed Balta, Efe C. Lygeros, John Smith, Roy S. |
| contents | It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identification of continuous-time stochastic parametric Wiener models through stochastic approximation. The method is applicable to generic model parameterizations and, as demonstrated in the numerical simulation examples, it is robust with respect to the assumptions on the spectrum of the disturbance process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data Abdalmoaty, Mohamed Balta, Efe C. Lygeros, John Smith, Roy S. Methodology Machine Learning Systems and Control Signal Processing It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators. On the other hand, optimal statistical identification, via likelihood-based methods, is sensitive to the assumptions on the data distribution and is usually based on relatively complex sequential Monte Carlo algorithms. We develop a simple recursive online estimation algorithm based on an output-error predictor, for the identification of continuous-time stochastic parametric Wiener models through stochastic approximation. The method is applicable to generic model parameterizations and, as demonstrated in the numerical simulation examples, it is robust with respect to the assumptions on the spectrum of the disturbance process. |
| title | Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data |
| topic | Methodology Machine Learning Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2403.05899 |