基于遗传BP神经网络的装甲装备器材需求预测  

Research on Armored Equipment Demand Forecasting Based on BP Neural Network

作　　者：可荣博[1] 王铁宁[1] 宋宁波[2] 

机构地区：[1]装甲兵工程学院,北京100072 [2]北方自动控制技术研究所,太原030006

出　　处：《火力与指挥控制》2015年第6期99-102,共4页Fire Control & Command Control

摘　　要：装甲装备器材保障具有规模大、时间紧、消耗大、不确定因素多、决策难度大等特点。准确的需求预测是实施主动的、精细化的器材保障的重要前提条件。利用BP神经网络较强自学习能力和自适应能力对器材需求规律进行学习,并借助遗传算法提高BP神经网络的收敛速度,设计了一种基于遗传算法改进的BP神经网络模型预测方法,对装甲装备器材进行需求预测。通过实例计算表明,该方法比单纯BP神经网络方法具有预测精度高、收敛速度快的优点。Armored equipment material support has some particular features,including large scale,time urgency,large consumption,a lot of uncertain factors and difficult decision. Accurate demand forecasting is an important prerequisite to implement an initiative and refinement equipment protection.In this paper,BP neural network learrning and self-adaptive ability is used to learn the law of equipment demand,genetic algorithm is used to improve BP neural network convergence speed. A genetic algorithm improved BP neural network algorithm is proposed for forecasting equipment demond.The experiments show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

关 键 词：装甲装备器材 遗传算法 BP神经网络 

分 类 号：TJ811[兵器科学与技术—武器系统与运用工程] N945[自然科学总论—系统科学]