contributor author | Yang, Yejiang | |
contributor author | Mo, Zihao | |
contributor author | Xiang, Weiming | |
date accessioned | 2025-04-21T10:32:00Z | |
date available | 2025-04-21T10:32:00Z | |
date copyright | 10/3/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2689-6117 | |
identifier other | aldsc_5_1_011001.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4306389 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Efficient Neural Hybrid System Learning and Interpretable Transition System Abstraction for Dynamical Systems1 | |
type | Journal Paper | |
journal volume | 5 | |
journal issue | 1 | |
journal title | ASME Letters in Dynamic Systems and Control | |
identifier doi | 10.1115/1.4066516 | |
journal fristpage | 11001-1 | |
journal lastpage | 11001-7 | |
page | 7 | |
tree | ASME Letters in Dynamic Systems and Control:;2024:;volume( 005 ):;issue: 001 | |
contenttype | Fulltext | |