针对神经网络预测导弹备件消耗时参数过多会导致事件过长并易陷入局部最优的问题,建立一种基于粗糙集和BP神经网络的导弹备件消耗预测模型.在对采集到的导弹备件消耗信息进行特征提取、形成决策表的基础上,用粗糙集理论对原始信息表进行约简,去除冗余的属性和属性值,并将约简的影响因素值输入到BP神经网络中进行训练预测.实例结果表明:该预测方法大大减少了网络的收敛时间,提高了模型的预测精度,为导弹备件消耗预测提供了一个新的思路.%When neural network forecasts missile spare parts, redundant parameter is prone to making event too long and getting into part optimization, in order to solve these problems, established a consumption forecasting model of missile spare parts based on rough sets and BP neural network. Firstly, consumption information of missile spare parts was abstracted and made into decision-making table; Secondly, simplified original information table and deleted redundant property and property value by rough sets theory; Lastly, the simplified influence factor value was put into BP neural network to carry out training and forecasting. The example results proved the consumption forecasting method reduced greatly convergence time of neural network, improved forecast precision, and afforded a new way for consumption forecasting of missile spare parts.
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