CAJ | 학술논문

为解决发动机的寿命预测精度问题，该文将贝叶斯( Bayesian )推断应用于最小二乘支持向量机( LS-SVM)模型参数的选择，建立了发动机在翼寿命的非线性预测模型。分析了影响发动机在翼寿命的性能参数，建立了用于机器学习的预测模型训练集，构建了基于LS-SVM的发动机在翼寿命预测模型。采用Bayesian推断理论优化LS-SVM建模，获取最优建模参数。通过某型发动机在翼寿命数据集训练模型，对在翼寿命进行预测。与几种常用的算法相比较，该文模型预测精度能够提高4.58%至9.51%，较好地解决了小样本下的预测问题，具有良好的泛化能力和预测精度。
위해결발동궤적수명예측정도문제，해문장패협사( Bayesian )추단응용우최소이승지지향량궤( LS-SVM)모형삼수적선택，건립료발동궤재익수명적비선성예측모형。분석료영향발동궤재익수명적성능삼수，건립료용우궤기학습적예측모형훈련집，구건료기우LS-SVM적발동궤재익수명예측모형。채용Bayesian추단이론우화LS-SVM건모，획취최우건모삼수。통과모형발동궤재익수명수거집훈련모형，대재익수명진행예측。여궤충상용적산법상비교，해문모형예측정도능구제고4.58%지9.51%，교호지해결료소양본하적예측문제，구유량호적범화능력화예측정도。
To resolve the problem of engine life forecasting accuracy,a nonlinear forecasting model for engine life on wing is established by applying Bayesian inference to the choices of model parameters of least squares support vector machine( LS-SVM) . The performance parameters affecting engine life on wing are analyzed,a forecasting model training set for machine study is established, and a forecasting model of engine life on wing is established based on the LS-SVM. The LS-SVM model is optimized by using Bayesian inference,and the best modeling parameters are obtained. The engine life on wing is forecasted by using a data set training model of a certain engine life on wing. Compared with several common algorithms,the forecasting accuracies of the model proposed here increase by 4. 58% -9. 51%,which solves the problem of forecasting of small samples,and performs well in generalization ability and forecasting precision.