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dc.contributor.authorLiu, Sifeng
dc.contributor.authorLin, Changhai
dc.contributor.authorTao, Liangyan
dc.contributor.authorJaved, Saad Ahmed
dc.contributor.authorFang, Zhigeng
dc.contributor.authorYang, Yingjie
dc.date.accessioned2020-07-07T10:36:16Z
dc.date.available2020-07-07T10:36:16Z
dc.date.issued2020-03-15
dc.identifier.citationLiu, S., Lin, C., Tao, L., Javed, S. A., Fang, Z. and Yang, Y. (2020) On spectral analysis and new research directions in grey system theory, Journal of Grey System, 32(1), pp.108-117.en
dc.identifier.issn0957-3720
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/19945
dc.descriptionThe file attached to this record is the Publisher's version.en
dc.description.abstractSpectral analysis as a powerful mean to identify the characteristics of data series is introduced in this paper. The problems requiring further explorations in grey system theory are also identified. This includes discrimination of various factors of a data sequence in frequency domain, spectral analysis of various sequence operators, the synthesis axiom of degree of greyness for “multiplication” and “division” etc. Further, how to select and test a grey prediction model? How to select and test an inverse grey incidence analysis model? The test rules and criteria of grey clustering evaluation models, etc.en
dc.language.isoenen
dc.publisherResearch Information Ltden
dc.subjectGrey system theoryen
dc.subjectNew research directionen
dc.subjectGrowth pointen
dc.subjectSpectrum analysisen
dc.subjectSequence operatoren
dc.subjectOperations of grey numbersen
dc.subjectGrey prediction modelen
dc.subjectGrey incidence analysis modelen
dc.subjectGrey clustering evaluation modelen
dc.titleOn Spectral Analysis and New Research Directions in Grey System Theoryen
dc.typeArticleen
dc.peerreviewedYesen
dc.funderLeverhulme Trusten
dc.projectidIEC\NSFC\170391en
dc.projectidIN-2014–020en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2020-01-12
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherRoyal Societyen


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