基于灰色神经网络的航空装备作战携行量预测  

The Prediction of Aviation Equipment Carried Quantity in Wartime Based on Gery Neural Network

作　　者：邵雨晗 辛后居[1] 高辉[1] 徐启丰[1] SHAO Yu-han XIN Hou-ju GAO Hui XU Qi-feng(Department of Air Material Management, Air Force Logistics College, Xuzhou 221000, China)

机构地区：[1]空军勤务学院航材管理系,江苏徐州221000

出　　处：《数学的实践与认识》2016年第20期62-68,共7页Mathematics in Practice and Theory

基　　金：空军装备部基金项目(KJ2014023600A11277);空军勤务学院研究生立项课题资助(KQY2014-14)

摘　　要：当前局势多变且节奏迅速的现代高新技术战争使得航空装备需求量与损耗率激增,航空装备保障人员也面临着战时保障情况复杂、决策难度增加的问题.在BP神经网络的基础上使用灰色理论对其进行了优化,将得到的灰色BP神经网络对航空装备作战携行数量进行了预测并与一般BP神经网络和GM(1,1)模型预测结果对比.结果表明:灰色BP神经网络预测精度高、收敛速度快、所需样本数据少,对航空装备作战携行数量预测具有重要价值.Changeable and rapid high-tech war makes the demand and attrition rate of aviation equipment soar.The support personnel of aviation equipment are facing the problem of more complex support situation and harder decision making in wartime.This paper uses grey theory optimization on the basis of BP neural network and works out the grey BP neural network for aviation equipment carried quantity prediction in wartime,the consequence is also compared with normal BP neural network and GM（1,1） model prediction.The results show that the grey BP neural network has the advantages of high prediction precision and fast convergence rate as well as less sample data required,which has a significant value on aviation equipment carried quantity prediction in wartime.

关 键 词：灰色神经网络 航空装备 作战保障 携行预测 

分 类 号：E926[军事—军事装备学] TP183[兵器科学与技术—武器系统与运用工程]