火力与指挥控制Issue(9)：91-95,100,6.
基于灰色神经网络的装甲部队油料消耗预测
Fuel Consumption of Armored Forces Forecasting Based on Gray Theory and Neural Network
夏秀峰 1刘权羲1
作者信息
- 1. 沈阳航空航天大学计算机学院，沈阳 110136
- 折叠
摘要
Abstract
The oil is blood of the modern warfare,accurately predicting the fuel consumption of war directly enhance the capacity of the fuel logistics support . The traditional prediction model of fuel consumption is not accurate enough, and there are some limitations in the range of applications,it is difficult to meet the exact security needs of information warfare. Propose an armored force fuel consumption forecast combination model,statistical analysis of historical fuel consumption data and fuel consumption impact factors,calculate the gray relational grade of influencing factors and fuel consumption as weight coefficient;use gm(1,1)model to predict the fuel consumption of a force`s nest military action;use the predictive value of GM(1,1)model,the weighted value of each factor and fuel consumption of the actual value to train the network;predict the fuel consumption of next military action. The average relative error calculation shows that combination forecasting model is more accuracy than single GM (1,1)prediction model,it can better guide the troops into the next phase of the fuel supply management.关键词
装甲部队/油耗预测/灰色系统理论/神经网络/组合模型Key words
armored forces/fuel consumption prediction/gray theory/neural network/combination model分类
信息技术与安全科学引用本文复制引用
夏秀峰,刘权羲..基于灰色神经网络的装甲部队油料消耗预测[J].火力与指挥控制,2014,(9):91-95,100,6.
