基于主成分分析-RBF神经网络模型的备件预测研究

备件预测在产品物流保障中占有极其重要的地位，针对现有各种航空备件预测方法精度较低，无法满足实际需求的现状，文章提出了基于主成分分析-RBF神经网络模型的备件预测方法：首先利用主成分分析方法去除原始输入层数据的相关性，以解决RBF神经网络模拟预测备件需求时输入变量过多，网络规模过大导致效率下降的问题．最后选择合适的径向基函数密度训练神经网络。通过结合实例进行分析，取得了较好的效果。

The Forcasting Research for Spare Parts Based on Principal Component Analysis and RBF Artificial Neural Network

Spare parts prediction stands a very important status in production logistic guarantee.Existing aviation material prediction approach has a low precision which can't meet the actual need.According to the problem, the forecasting approach for spare parts based on principal component analysis and artificial neural network was given.Firstly the approach can wipe off the correlation of the initial input data, in order to solve the problem that RBF network has too many input factor when predicting and then the e...