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dc.contributor.authorDong, Yuchengen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.contributor.authorCabrerizo, F. J.en
dc.contributor.authorDing, Zhaogangen
dc.contributor.authorLiang, Haimingen
dc.date.accessioned2016-07-05T10:06:28Z
dc.date.available2016-07-05T10:06:28Z
dc.date.issued2016
dc.identifier.citationDing, Z. et al. (2016) An opinion control rule with minimum adjustments to support the consensus reaching in bounded confidence model. Accepted for presentation at ITQM 2016en
dc.identifier.urihttp://hdl.handle.net/2086/12244
dc.description.abstractOpinion dynamics provides a modeling tool for the public opinion management. The existing studies mainly focused on building the evolution model of opinions. However, the control of public opinions has been a key problem in practical opinion dynamics. The objective of this paper is to propose an opinion control rule to support the consensus reaching. Based on the bounded confidence model, the consensus model with the minimum adjustment is proposed. Next, based on the proposed consensus model, we propose the opinion control rule to support the consensus reaching. Furthermore, a numerical example is given to illustrate the feasibility of the proposed opinion control rule. Through simulation experiments, we investigate the effects of adjustment thresholds and bounded confidences on the opinion control rule.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectOpinion dynamicsen
dc.subjectopinion control ruleen
dc.subjectconsensus modelen
dc.subjectminimum adjustmentsen
dc.subjectbounded confidenceen
dc.titleAn opinion control rule with minimum adjustments to support the consensus reaching in bounded confidence modelen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.procs.2016.07.154
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderThis study is supported by in part by NSF of China under Grants Nos. 71171160 and 71571124, FEDER funds under Grant TIN2013-40658-P, and the Andalusian Excellence Project Granten
dc.projectidTHis study is supported by in part by NSF of China under Grants Nos. 71171160 and 71571124, FEDER funds under Grant TIN2013-40658-P, and the Andalusian Excellence Project Granten
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2016-05-17en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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