A Novel Method for Optimizing Fractional Grey Prediction Model
Asian Research Journal of Mathematics · pp. 1–15 · Published 28 May 2019
10.9734/arjom/2019/v13i430115Abstract
Aiming at the shortcoming that the classical FGM(1,1) model regards the gray action quantity as a fixed constant, the DGM(1,1) model is used to dynamically simulate and predict the gray action quantity, so that the gray action quantity can change dynamically with time. On this basis, a new FGM(1,1,b) model with dynamic gray quantity change with time is proposed, and the total primary energy consumption in the Middle East is taken as a numerical example for simulation prediction. The results show that the prediction accuracy of the dynamic FGM(1,1,b) model proposed in this paper is higher than that of the classical FGM(1,1) model, and the practicability and effectiveness of the FGM(1,1,b) model are verified. At the same time, it also provides relevant theoretical basis for the study of world energy development.
Cited by 1
Jing Zhang, Zhenqiang Mi, Yu Guo · Neural Computing and Applications · 2021
Related research
- Combining a Continuous Search Algorithm with a Discrete Search Algorithm for Solving Non-linear Bi-level Programming Problem — shares topic coverage
- A New Background Value Improvement of Fractional Order Accumulated FAGM(1,1) Model and Its Application — shares topic coverage
- A Novel Dynamic Grey Action Quantity GM(1,1,b) Model and Its Application — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
1
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.