基于过程神经网络的时间序列预测及其应用研究

Time Series Prediction Based on Process Neural Networks and Its Applications

针对传统时间序列预测方法难以表达时间序列中时间累积效应的缺陷,提出一种基于过程神经网络的时间序列预测方法.就时间序列的短期预测和长期预测问题分别应用该方法建立了两种预测模型,并给出了相应的学习算法.以航空发动机状态监控中滑油铁金属含量预测为例验证了两种预测模型及其学习算法的有效性,并得到了满意的结果.

To the difficulty of expression of the temporal accumulation in the time series using conventional time series prediction methods, a time series prediction method based on process neural network is proposed. Time series short-term prediction model and long-term prediction model based on the proposed method are developed respectively, and the corresponding learning algorithms are given. The effectiveness of this two models and their learning algorithms are proved by the lubricating oil iron concentration prediction in the aircraft engine condition monitoring, and the test results are satisfactory.