dc.contributor.author | Zhang, Jianhua | |
dc.contributor.author | Yin, Zhong | |
dc.date.accessioned | 2022-05-03T13:30:19Z | |
dc.date.available | 2022-05-03T13:30:19Z | |
dc.date.created | 2022-02-18T16:13:44Z | |
dc.date.issued | 2021-04-14 | |
dc.identifier.citation | IFAC-PapersOnLine. 2020, 53 (2), 10084-10089. | en_US |
dc.identifier.issn | 2405-8963 | |
dc.identifier.uri | https://hdl.handle.net/11250/2993979 | |
dc.description.abstract | In this paper, physiological signals were measured from five participants, each participating in two sessions of experiment with identical experimental procedure. A simulation platform, AutoCAMS (Automation-enhanced Cabin Air Management System), was used to simulate a complex task environment of human-machine shared process control. Fuzzy models were constructed to quantitatively predict the human operator performance based on three EEG input features. The incremental-PID-controlled particle swarm optimization (IPID-PSO) algorithm was utilized to optimize the parameters of fuzzy models. The IPID-PSO algorithm incorporated incremental-PID-controlled search strategy to speed up the convergence of standard PSO algorithm. The operator performance modeling results are given to show the effectiveness of the IPID-PSO-tuned fuzzy modeling approach proposed to momentary operator performance assessment problem under consideration. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | International Federation of Automatic Control | en_US |
dc.relation.ispartofseries | IFAC-PapersOnLine;Volume 53, Issue 2, 2020 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.subject | Operator functional states | en_US |
dc.subject | Human performances | en_US |
dc.subject | Human-machine systems | en_US |
dc.subject | Physiological signals | en_US |
dc.subject | EEG signals | en_US |
dc.subject | Particle swarm optimization | en_US |
dc.subject | Fuzzy modeling | en_US |
dc.title | Operator Performance Prediction based on Fuzzy Modeling Approach | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2020 The Authors | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | https://doi.org/10.1016/j.ifacol.2020.12.2731 | |
dc.identifier.cristin | 2003487 | |
dc.source.journal | IFAC-PapersOnLine | en_US |
dc.source.volume | 53 | en_US |
dc.source.issue | 2 | en_US |
dc.source.pagenumber | 10084-10089 | en_US |