在线时间序列预测方法及其应用

摘要

为解决非线性复杂时间序列在线预测问题,提出了一种基于过程神经网络模型的在线预测方法.首先,在历史数据的基础上建立双并联离散过程神经网络模型;然后,根据在线更新的数据样本,采用递推极限学习算法对过程神经网络隐层到输出层的权值进行相应的更新;最后,应用权值更新后的过程神经网络模型对时间序列进行预测.文中给出了具体的过程神经网络学习算法与权值更新机制,并以混沌时间序列与液体火箭发动机的状态预测为例对方法进行了验证.研究结果表明:该方法在预测精度和适应能力上较单一的离线模型有显著提高,可以为非线性复杂时间序列在线预测问题提供一种有效的解决方法.%Aiming at the issue about online prediction of the complicated nonlinear time series, an online prediction method based on process neural network ( PNN) was proposed. The prediction model of double parallel feedforward discrete input process neural network ( DPFDPNN ) , which was trained by off-line data, was firstly established to make prediction of the complicated nonlinear time series. In order to improve the accuracy and efficiency of the DPFDPNN for the time series prediction, the weight connecting, the hidden layer and output layer were then directly updated by the recursion extreme learning algorithm ( REL) based on recursive algorithm with the real data stream. Finally, the DPFDPNN with the updated weight was adopted to predict the time series. The corresponding learning algorithm for DPFDPNN and the updating mechanisms were obtained in this paper. The prediction method mentioned above was verified by a test case with chaotic time series, and an example of method application in condition pre-diction of liquid propellant rocket engine was given. The results show that the proposed method outperformed the DPFDPNN without weight updated at accuracy and adaptability. It ' s an effective way to solve the failure online prediction problem of the complicated nonlinear time series.