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dc.contributor.authorYe, Jingen
dc.contributor.authorDang, Yaoguoen
dc.contributor.authorWang, Junjieen
dc.contributor.authorYang, Yingjieen
dc.date.accessioned2018-01-24T11:55:47Z
dc.date.available2018-01-24T11:55:47Z
dc.date.issued2017
dc.identifier.citationYe, J. et al. (2017) Grey prediction model of interval grey numbers based on a novel compound function transformation, The Journal of Grey System, 29(4), pp.154-175.en
dc.identifier.issn0957-3720
dc.identifier.urihttp://hdl.handle.net/2086/15098
dc.descriptionThe file attached to this record is the author's final peer reviewed version.en
dc.description.abstractFocusing on the prediction accuracy of interval grey numbers, the primary goal of this paper is to investigate novel grey prediction models to predict four typical kinds of interval grey numbers sequences respectively. The models used in this study can be briefly described as a combination of function transformation and Grey Model (GM) of interval grey numbers. According to different interval grey numbers sequences, the approaches can be applied to the upper bound and lower bound sequences of interval grey numbers or the kernel and measurement sequences of interval grey numbers. Finally, these new improved models have been verified by numerical examples and cases to demonstrate their validity and practicability. It proves these new models are not only applicable for increasingly high growth sequences where traditional grey models are not effective, but also control the enlargement of grey degrees of interval grey numbers which is very important for interval grey numbers’ forecasting. In summary, the paper effectively extends function transformation technology to the field of interval grey numbers for grey prediction.en
dc.language.isoenen
dc.publisherResearch Information Ltd.en
dc.subjectInterval Grey Numberen
dc.subjectFunction Transformationen
dc.subjectDataen
dc.titleGrey prediction model of interval grey numbers based on a novel compound function transformationen
dc.typeArticleen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderLeverhulmeen
dc.projectidIN-2014-020en
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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