Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (3): 593-601.doi: 10.23919/JSEE.2020.000024

• Systems Engineering • Previous Articles     Next Articles

Improvement and application of GM(1, 1) model based on multivariable dynamic optimization

Yuhong WANG(), Jie LU*()   

  • Received:2019-03-25 Online:2020-06-30 Published:2020-06-30
  • Contact: Jie LU E-mail:yuhongwang@jiangnan.edu.cn;JIE0327@outlook.com
  • About author:WANG Yuhong was born in 1979. He received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics in 2010. Now he is a professor of School of Business, Jiangnan University. His research interests include uncertainty system prediction and decision method, and grey system theory. E-mail: yuhongwang@jiangnan.edu.cn|LU Jie was born in 1993. He received his M.S. degree from Jiangnan University in 2019. His research interests include uncertainty system prediction and decision method, and grey system theory. E-mail: JIE0327@outlook.com
  • Supported by:
    the National Natural Science Foundation of China(71871106);the Blue and Green Project in Jiangsu Province, and the Six Talent Peaks Project in Jiangsu Province(2016-JY-011);This paper was supported by the National Natural Science Foundation of China (71871106), the Blue and Green Project in Jiangsu Province, and the Six Talent Peaks Project in Jiangsu Province (2016-JY-011)

Abstract:

For the classical GM(1, 1) model, the prediction accuracy is not high, and the optimization of the initial and background values is one-sided. In this paper, the Lagrange mean value theorem is used to construct the background value as a variable related to $\boldsymbol k$. At the same time, the initial value is set as a variable, and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error (MRE). Combined with the domestic natural gas annual consumption data, the classical model and the improved GM(1, 1) model are applied to the calculation and error comparison respectively. It proves that the improved model is better than any other models.

Key words: grey prediction, GM(1, 1) model, background value, grey system theory