动态贝叶斯最小二乘支持向量机

为了对涡扇发动机的运行参数变化进行实时高精度预测，提出一种基于动态贝叶斯最小二乘支持向量机(LS-SVM)的时间序列预测算法。该算法将贝叶斯证据框架理论用于推断LS-SVM的初始模型参数；然后，利用样本增减迭代学习算法实现LS-SVM的参数动态调整。对某型涡扇发动机的摩擦力矩时间序列进行动态预测，并与动态LS-SVM模型的预测结果进行比较。结果显示，动态贝叶斯LS-SVM具有较好的预测精度。

Dynamic Bayesian least squares support vector machine

A time series prediction method based on the dynamic Bayesian least squares support vector machine(LS-SVM) is proposed to accurately predict operating parameters of the turbofan engine. By the Bayesian evidence framework theory, initial model parameters of the LS-SVM are inferred. Dynamic learning of the LS-SVM and dynamic prediction of time series are realized by the recursively incremental and decremental sample learning method. The method is applied to dynamically predict frictional moment time series of a certain type of the turbofan engine, and a comparison is made with the dynamic LS-SVM. The results show that the proposed method is effective in improving prediction accuracy.