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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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