基于回声状态网络的飞机混沌时间序列预测模型

准确检测飞机即将发生的故障或预测其状态的变化趋势,对于实现飞行安全具有重要意义。文章针对传统基于回声状态网络在故障预测中的不足,构建了基于小波降噪的回声状态网络预测模型。该模型保留了非线性时间序列回声状态网络预测的优势,并采取小波变换对混沌时间序列进行降噪预处理,有效提高了含噪混沌时间序列的预测精度。论文通过对某飞机发动机滑油散热器温度时间序列数据序列进行预测分析,表明文中模型具有较好的预测精度,验证了模型的有效性。

An Effective Prediction Model for Aircraft Chaos Time Series Based on Echo State Networks (ESN)

It is significant for flight safety to accurately detect the coming fault of aircraft or predict its change trend.Aiming at suppressing the shortcoming of fault prediction based on traditional ESN,we present a new prediction method combining ESN with wavelet denoising.Sections 1 and 2 of the full paper explain our prediction model mentioned in the title,which we believe is effective and whose core is: the method not only reserves the advantages of ESN model in nonlinear time series prediction but also reduces the noise influence in practice,i.e.,the pretreatment via wavelet transform will be done before prediction.Section 3 concerns a certain type of aero-engine lubricator.Its simulation results are presented in Figs.4,5,7,8 and Tables 1 and 2.The simulation results and their analysis show preliminarily that the proposed method improves the prediction accuracy of nonlinear chaotic time series including noises,thus indicating that the proposed model is an effective approach in actual application.