مؤتمر
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
| العنوان: | Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering |
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| المؤلفون: | Imbiriba, Tales, Demirkaya, Ahmet, Duník, Jindřich, Straka, Ondřej, Erdoğmuş, Deniz, Closas, Pau |
| بيانات النشر: | IEEE |
| سنة النشر: | 2022 |
| المجموعة: | University of West Bohemia Digital Library / Digitální knihovna Západočeské univerzity v Plzni |
| مصطلحات موضوعية: | Nonlinear filtering, Target tracking, Hybrid Neural Network, Physics-based Neural Models, Gaussian filtering |
| الوصف: | In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows for model adaptation when new operation conditions come into play or the physics-based model is insufficient (or incomplete) to properly describe the latent phenomenon. One advantage of the APBMs and our estimation procedure is the capability of maintaining the physical interpretability of estimated states. Furthermore, we propose a constraint filtering approach to control the neural network contributions to the overall model. We also exploit assumed density filtering techniques and cubature integration rules to present a flexible estimation strategy that can easily deal with nonlinear models and high-dimensional latent spaces. Finally, we demonstrate the efficacy of our methodology by leveraging a target tracking scenario with nonlinear and incomplete measurement and acceleration models, respectively. |
| نوع الوثيقة: | conference object |
| وصف الملف: | 6 s.; application/pdf |
| اللغة: | English |
| ردمك: | 978-1-73774-972-1 1-73774-972-6 |
| العلاقة: | Proceedings of the 25th International Conference on Information Fusion, FUSION 2022; IMBIRIBA, T. DEMIRKAYA, A. DUNÍK, J. STRAKA, O. ERDOĞMUŞ, D. CLOSAS, P. Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering. In Proceedings of the 25th International Conference on Information Fusion, FUSION 2022. Linköping, Sweden: IEEE, 2022. s. 1-6. ISBN: 978-1-73774-972-1 , ISSN: neuvedeno; neuvedeno; 2-s2.0-85136554327; http://hdl.handle.net/11025/51459Test; 855689000065 |
| DOI: | 10.23919/FUSION49751.2022.9841291 |
| الإتاحة: | https://doi.org/10.23919/FUSION49751.2022.9841291Test http://hdl.handle.net/11025/51459Test |
| حقوق: | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. ; © IEEE ; restrictedAccess |
| رقم الانضمام: | edsbas.C824E16B |
| قاعدة البيانات: | BASE |
| ردمك: | 9781737749721 1737749726 |
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| DOI: | 10.23919/FUSION49751.2022.9841291 |