Browsing by Author "Dong, Aoxiang"

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    End-to-end identification of autoregressive with exogenous input (ARX) models using neural networks
    (Springer, 2025-02-01) Dong, Aoxiang; Starr, Andrew; Zhao, Yifan
    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 identification, 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.
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    Neural network-based parametric system identification: a review
    (Taylor and Francis, 2023-08-02) Dong, Aoxiang; Starr, Andrew; Zhao, Yifan
    Parametric system identification, which is the process of uncovering the inherent dynamics of a system based on the model built with the observed inputs and outputs data, has been intensively studied in the past few decades. Recent years have seen a surge in the use of neural networks (NNs) in system identification, owing to their high approximation capability, less reliance on prior knowledge, and the growth of computational power. However, there is a lack of review on neural network modelling in the paradigm of parametric system identification, particularly in the time domain. This article discussed the connection in principle between conventional parametric models and three types of NNs including Feedforward Neural Networks, Recurrent Neural Networks and Encoder-Decoder. Then it reviewed the advantages and limitations of related research in addressing two major challenges of parametric system identification, including the model interpretability and modelling with nonstationary realisations. Finally, new challenges and future trends in neural network-based parametric system identification are presented in this article.