基于神经网络的故障率预测方法

 为了更好地预测产品故障率，提出了基于神经网络的故障率预测方法，分别给出了基于反向传播(BP)网络和径向基函数(RBF)网络进行故障率预测的基本思想、预测模型和实施步骤。分别对比分析了神经网络法与回归分析法、分解分析法、移动平均法、指数平滑法、自适应过滤法、自回归移动平均混合(ARMA)模型等统计预测方法的区别，对照故障率的特点，说明了神经网络法是其中最适用于故障率预测的统计方法。最后分别按这两种模型对某航空公司波音飞机故障率进行了预测，预测结果表明：这两种模型均适用于故障率预测，预测值与真实值的误差在20%之内，且RBF网络的预测效果略优于BP网络，此外通过与上述统计预测法的误差进行对比，说明神经网络法预测误差最小。

Failure Rate Forecasting Method Based on Neural Networks

To forecast failure rates better,a method for failure rate forecasting based on artificial neural networks is advanced.The basic ideas,forecasting models and steps of failure rate forecasting based on back propagation(BP) network and radial basis function(RBF) network are discussed respectively.The differences between neural network and other statistical forecasting methods,such as regression analysis,decomposition analysis,moving average,exponential smoothing,self-adaptive filtering and auto-regressive moving average(ARMA) model,are compared respectively.The conclusion is drawn that neural network is the best statistical method of them according to the characteristics of failure rates.The failure rates of Boeing flights in a certain airline company are forecasted according to these two models.Results show that both of these models are suitable for failure rate forecasting,errors between predicted value and observed value are less than 20%,and the effect of failure rate forecasting based on RBF network is better than that based on BP network.By compared to errors of those statistical methods,errors of failure rate forecasting based on neural networks are much smaller.