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New optimized grey derivative models for grain production forecasting in China

Published online by Cambridge University Press:  05 March 2014

L. LIU ,
Y. WANG ,
J. WU ,
J. WANG  and
C. XI
L. LIU
Affiliation:
School of Economics and Management, North China Electric Power University, Beijing 102206, China
Y. WANG*
Affiliation:
School of Science, Ningbo University of Technology, Ningbo 315211, China
J. WU
Affiliation:
School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
J. WANG
Affiliation:
School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
C. XI
Affiliation:
School of Mathematics & Statistics, Lanzhou University, Lanzhou 730000, China
*
*To whom all correspondence should be addressed. Email: wangyuanyuan1021@hotmail.com
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Summary

Although the grey forecasting model has been successfully employed in various fields and demonstrates promising results, the literature shows that its performance could still be improved. Therefore, the aim of the present study was to continue the investigation and derive three hybrid models to predict grain production in China by combining particle swarm optimization (PSO) with the grey linear power index model, the grey logarithm power model and the grey parabola power model. In grey modelling, the use of PSO had the ability to search optimum grey parameters to construct three improved derivative grey models. The results concluded that the improved optimization models with high precision were superior to the traditional models, and PSO contributed more to precision improvement of the three grey models. Furthermore, results from the experiments demonstrated that the optimized models were reliable and valid.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 153 , Issue 2 , March 2015 , pp. 257 - 269
Copyright
Copyright © Cambridge University Press 2014 

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