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dc.contributor.authorGuo, X.en
dc.contributor.authorLiu, S.en
dc.contributor.authorWu, L.en
dc.contributor.authorGao, Y.en
dc.contributor.authorYang, Yingjieen
dc.identifier.citationGuo, X., Liu, S., Wu, L., Gao, Y. and Yang, Y. (2015) A multi-variable grey model with a self-memory component and its application on engineering prediction. Engineering Applications of Artificial Intelligence, 42, pp.82-93.en
dc.description.abstractThis paper presents a novel multi-variable grey self-memory coupling prediction model (SMGM(1,m)) for use in multi-variable systems with interactional relationship under the condition of small sample size. The proposed model can uniformly describe the relationships among system variables and improve the modeling accuracy. The SMGM(1,m) model combines the advantages of the self-memory principle of dynamic system and traditional MGM(1,m) model through coupling of the above two prediction methods. The weakness of the traditional grey prediction model, i.e., being sensitive to initial value, can be overcome by using multi-time-point initial field instead of only single-time-point initial field in the system׳s self-memorization equation. As shown in the two case studies of engineering settlement deformation prediction, the novel SMGM(1,m) model can take full advantage of the system׳s multi-time historical monitoring data and accurately predict the system׳s evolutionary trend. Three popular accuracy test criteria are adopted to test and verify the reliability and stability of the SMGM(1,m) model, and its superior predictive performance over other traditional grey prediction models. The results show that the proposed SMGM(1,m) model enriches grey prediction theory, and can be applied to other similar multi-variable engineering systems.en
dc.subjectGrey prediction theoryen
dc.subjectMulti-variable systemen
dc.subjectMGM(1,m) modelen
dc.subjectSelf-memory principleen
dc.subjectSubgrade settlementen
dc.subjectFoundation pit deformationen
dc.titleA multi-variable grey model with a self-memory component and its application on engineering predictionen
dc.researchgroupCentre for Computational Intelligenceen
dc.funderFP7 and Leverhulme Trusten
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en

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