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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.10240 |
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| _version_ | 1866909391487238144 |
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| author | Yang, Yejiang Mo, Zihao Xiang, Weiming |
| author_facet | Yang, Yejiang Mo, Zihao Xiang, Weiming |
| contents | This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient way of dynamics learning and system identification. First, a low-level model will be trained to learn the system dynamics, which utilizes multiple simple neural networks to approximate the local dynamics generated from data-driven partitions. Then, based on the low-level model, a high-level model will be trained to abstract the low-level neural hybrid system model into a transition system that allows Computational Tree Logic Verification to promote the model's ability with human interaction and verification efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10240 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems Yang, Yejiang Mo, Zihao Xiang, Weiming Systems and Control Machine Learning This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient way of dynamics learning and system identification. First, a low-level model will be trained to learn the system dynamics, which utilizes multiple simple neural networks to approximate the local dynamics generated from data-driven partitions. Then, based on the low-level model, a high-level model will be trained to abstract the low-level neural hybrid system model into a transition system that allows Computational Tree Logic Verification to promote the model's ability with human interaction and verification efficiency. |
| title | Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2411.10240 |