摘要
装备性能参数预测是装备系统故障预测与健康管理的重要组成部分,对于提高装备保障效能有重大意义。本文提出了一种基于灰色模型和改进小波神经网络的组合预测模型。在灰色预测的基础上,训练小波神经网络进行灰色预测的残差修正,并通过对小波神经网络的改进提高了网络学习效率。对某型雷达中频接受单元的压控振荡器输出频率进行预测,实验证明,该组合模型结合了灰色预测和改进小波神经网络的优点,有较高预测精度和泛化能力。将该组合模型应用于装备状态参数预测具有可行性。
Prediction of equipment performance parameter is an important part of PHM（prognostics and health management）,which has great significance for improving the efficiency of equipment support.This dissertation proposed a combined model,which is based on grey model and improved wavelet neural network.The residual of grey prediction is trained in the wavelet neural network to correct the consequence of prediction.Also the learning efficiency is enhanced by the improvement of wavelet neural network.By analyzing the output frequency data of VCO in a intermediate frequency unit for a certain radar,the experimental results shows that this combined model has higher prediction accuracy and generalization ability.Therefore it＇s feasible to apply the combined model in the prediction of equipment performance parameter.
出处
《电子测量技术》
2016年第3期18-22,共5页
Electronic Measurement Technology
