Fuel Consumption of Armored Forces Forecasting Based on Gray Theory and Neural Network
Xia Xiu-fen · Fire Control and Command Control · 2014
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.