Neural network-based parametric system identification: a review

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dc.contributor.authorDong, Aoxiang
dc.contributor.authorStarr, Andrew
dc.contributor.authorZhao, Yifan
dc.date.accessioned2023-08-16T14:57:41Z
dc.date.available2023-08-16T14:57:41Z
dc.date.issued2023-08-02
dc.description.abstractParametric 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.en_UK
dc.identifier.citationDong A, Starr A, Zhao Y. (2023) Neural network-based parametric system identification: a review. International Journal of Systems Science, Available online 2 August 2023en_UK
dc.identifier.issn0020-7721
dc.identifier.urihttps://doi.org/10.1080/00207721.2023.2241957
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20107
dc.language.isoenen_UK
dc.publisherTaylor and Francisen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectGranger causalityen_UK
dc.subjectnonlinear system identificationen_UK
dc.subjectnonstationary time seriesen_UK
dc.subjectrobust and adaptive modellingen_UK
dc.subjectinterpretable deep learningen_UK
dc.titleNeural network-based parametric system identification: a reviewen_UK
dc.typeArticleen_UK

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