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dc.contributor.authorLi, Z.
dc.contributor.authorYang, Q.
dc.contributor.authorChen, S.
dc.contributor.authorZhou, W.
dc.contributor.authorChen, L.
dc.contributor.authorSong, Lei
dc.date.accessioned2022-04-12T00:49:58Z
dc.date.available2022-04-12T00:49:58Z
dc.date.issued2019-08-01
dc.identifier.issn1550-1329
dc.identifier.issn1550-1477
dc.identifier.urihttps://hdl.handle.net/10652/5664
dc.description.abstractThe study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions.en_NZ
dc.language.isoenen_NZ
dc.publisherSAGE Publicationsen_NZ
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectfatigue driving featuresen_NZ
dc.subjectfuzzy neural recurrent neural networken_NZ
dc.subjectsteering wheel angleen_NZ
dc.subjectrobust learningen_NZ
dc.subjectneural networksen_NZ
dc.titleA fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor dataen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2022-02-18T13:30:46Z
dc.rights.holder© authorsen_NZ
dc.identifier.doihttps://doi.org/10.1177/1550147719872452en_NZ
dc.subject.marsden460204 Fuzzy computationen_NZ
dc.subject.marsden461104 Neural networksen_NZ
dc.identifier.bibliographicCitationLi, Zuojin., Qing, Yang., Shengfu, Chen., Wei, Zhou., Liukui, Chen., & Lei, Song. (2019). A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data. International Journal of Distributed Sensor Networks, 15(9), 1-9. doi:https://doi.org/10.1177/1550147719872452en_NZ
unitec.publication.spage1en_NZ
unitec.publication.lpage9en_NZ
unitec.publication.volume15en_NZ
unitec.publication.issue9en_NZ
unitec.publication.titleInternational Journal of Distributed Sensor Networksen_NZ
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationChongqing University of Science and Technologyen_NZ
unitec.identifier.roms66829en_NZ
unitec.publication.placeNewbury Park, California, United Statesen_NZ
unitec.institution.studyareaComputingen_NZ


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