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作 者:Aoxiang Dong Andrew Starr Yifan Zhao
机构地区:[1]School of Aerospace,Transport and Manufacturing,Cranfield University,Cranfield MK430AL,UK
出 处:《Machine Intelligence Research》2025年第1期117-130,共14页机器智能研究(英文版)
摘 要:Traditional parametric system identification methods usually rely on apriori knowledge of the targeted system,which may not always be available,especially for complex systems.Although neural networks(NNs)have been increasingly adopted in system iden-tification,most studies have failed to derive interpretable parametric models for further analysis.In this paper,we propose a novel end-to-end autoregressive with exogenous input(ARX)model identification framework using NNs.An order-wise neural network structure is introduced and trained using a multitask learning approach to simultaneously identify both the model terms and coefficients of the ARX model.Through testing with various neural network backbones and training data sizes in different scenarios,we empirically demonstrate that the proposed framework can effectively identify an arbitrary stable ARX model with finite simulation training data.This study opens up a new research opportunity for parametric system identification by harnessing the power of deep learning.
关 键 词:Linear system identification and estimation learning systems model structure determination multivariable systems deep learning.
分 类 号:TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] G841[文化科学—体育训练]
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