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所属单位：民航学院

发表刊物：Hangkong Dongli Xuebao

摘要：A method to predict the change trend and space of aero-engine parameters with fuzzy information granulation (FIG) and optimized support vector machine (SVM) was put forward. FIG was adopted to granulate the parameters. Genetic algorithm (GA) was applied into adaptive selection of the best penalty parameter and kernel function parameter with K-fold cross validation (K-CV) error minimum as the optimization goal. The SVM model was trained for nonlinear prediction of fuzzy particles. The verification results of some airlines monitoring performance parameters data of an aero-engine showed that the algorithm proposed can effectively realize the change trend and spatial prediction of aero-engine performance parameters. In addition, the influence of window size on prediction accuracy and the effect of multi-step prediction were studied on the basis of instance. As a result, it was concluded that the best window size was three data and the forecasting error within three steps was less than 10%. © 2017, Editorial Department of Journal of Aerospace Power. All right reserved.

ISSN号：1000-8055

是否译文：否

发表时间：2017-12-01

合写作者：刘长江,曹愈远,张丽娜

通讯作者：李艳军