Research on the orders differential optimization and structural expansion of multidimensional grey model

YIN Fengfeng, ZENG Bo, YU Le'an, MAO Cuiwei, BAI Yun

Systems Engineering - Theory & Practice ›› 2023, Vol. 43 ›› Issue (7) : 2166-2178.

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Systems Engineering - Theory & Practice ›› 2023, Vol. 43 ›› Issue (7) : 2166-2178. DOI: 10.12011/SETP2022-2288

Research on the orders differential optimization and structural expansion of multidimensional grey model

  • YIN Fengfeng1, ZENG Bo1, YU Le'an2, MAO Cuiwei3, BAI Yun1
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Abstract

Currently, the multidimensional grey prediction model takes the same order of dependent and independent variables for modeling, ignoring the differences between data characteristics and physical properties of different variables, which leads to poor stability of model performance. To this end, the paper defines and optimizes the orders of dependent and independent variables differentially and introduces a nonlinear correction term to expand the grey information structure of the model, on which a new multidimensional grey prediction model is constructed. The new model is used to simulating and predicting the corrosion rate of oil and gas pipelines, and the results show that its comprehensive error (0.321%) is much smaller than the other three mainstream multidimensional grey prediction models (3.035%, 2.212% and 0.755% respectively), achieving an effective modeling of the corrosion rate of pipelines. The research results improve the pre-processing effect of orders on sequence data and the mining ability of data features, which have positive significance for improving the modeling ability of multidimensional grey prediction models.

Key words

multidimensional grey prediction model / differential optimization of orders / expansion of model structure / prediction of pipeline corrosion rate

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YIN Fengfeng , ZENG Bo , YU Le'an , MAO Cuiwei , BAI Yun. Research on the orders differential optimization and structural expansion of multidimensional grey model. Systems Engineering - Theory & Practice, 2023, 43(7): 2166-2178 https://doi.org/10.12011/SETP2022-2288

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Funding

National Natural Science Foundation of China (72071023); The Major Project of Science and Technology Research Program of Chongqing Education Commission of China (KJZD-M202300801); Chongqing Natural Science Foundation of China (CSTB2023NSCQ-MSX0365, CSTB2023NSCQ-MSX0380)
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