Ship Equipment Repair Cost Forecast Combination Based on Neural Network and GM(1,1)

被引：0
|
作者：
Li Fenghuan [1 ]
Liu Wenjun [2 ]
机构：
[1] Hubei Polytech Inst, Sch Logist, Xiaogan 432000, Peoples R China
[2] Ezhou Vocat Coll, Sch Mech Engn, Ezhou 436000, Peoples R China
来源：
PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INNOVATION AND MANAGEMENT | 2015年
关键词：
Ship Equipments Repair Cost; Forecast Combination; BP Neural Network; GM(1,1);
D O I：
暂无
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
The influencing factors of ship equipments repair cost are various, and its uncertainty is huge. It is very difficult to accurately predict ship equipments repair cost. The change of ship equipment repair cost is nonlinear. The traditional BP algorithm is improved by Levenberg-Marquardt optimize theory to adjust the network weights and thresholds. Meanwhile, the original value of traditional GM(1,1) is the first value of original data, which may affect forecast accuracy. The structure method of original value is improved by least gradient algorithm. On the base of this, the optimal forecast combination method is proposed to combine forecast results of the ahead two forecast models. Both the results of analysis and forecasting indicate the validity and feasibility of the researching methods.
引用
收藏
页码：821 / 825
页数：5
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