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    Efficient Neural Hybrid System Learning and Interpretable Transition System Abstraction for Dynamical Systems1

    Source: ASME Letters in Dynamic Systems and Control:;2024:;volume( 005 ):;issue: 001::page 11001-1
    Author:
    Yang, Yejiang
    ,
    Mo, Zihao
    ,
    Xiang, Weiming
    DOI: 10.1115/1.4066516
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This article proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient method of dynamics learning and system identification. First, a low-level model is 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 is trained to abstract the low-level neural hybrid system model into a transition system that allows computational tree logic (CTL) verification to promote model’s ability to handle human interaction and verification efficiency.
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      Efficient Neural Hybrid System Learning and Interpretable Transition System Abstraction for Dynamical Systems1

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4306389
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    contributor authorYang, Yejiang
    contributor authorMo, Zihao
    contributor authorXiang, Weiming
    date accessioned2025-04-21T10:32:00Z
    date available2025-04-21T10:32:00Z
    date copyright10/3/2024 12:00:00 AM
    date issued2024
    identifier issn2689-6117
    identifier otheraldsc_5_1_011001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4306389
    description abstractThis article proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient method of dynamics learning and system identification. First, a low-level model is 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 is trained to abstract the low-level neural hybrid system model into a transition system that allows computational tree logic (CTL) verification to promote model’s ability to handle human interaction and verification efficiency.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleEfficient Neural Hybrid System Learning and Interpretable Transition System Abstraction for Dynamical Systems1
    typeJournal Paper
    journal volume5
    journal issue1
    journal titleASME Letters in Dynamic Systems and Control
    identifier doi10.1115/1.4066516
    journal fristpage11001-1
    journal lastpage11001-7
    page7
    treeASME Letters in Dynamic Systems and Control:;2024:;volume( 005 ):;issue: 001
    contenttypeFulltext
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