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发表刊物：IEEE ACCESS

关键字：Neural network; real-time; dynamic adaptive model; support vector regression; data storage

摘要：A novel modeling method, which is based on a min-batch gradient descent neural network (MGD NN), is proposed to establish an adaptive dynamic model of a turbofan engine in a large flight envelope. For establishing a high precision engine dynamic model in a large flight envelope, it always needs a very big training data. This proposed method adopts the MGD algorithm, which is more suitable to train a neural network for big training data due to it consumes much less time to update NN parameters. Dramatically, the huger training data of the MGD NN is the better generalization performance it would be. Furthermore, a regularization strategy, which will also improve the generalization performance of the MGD NN, is applied here. Finally, compared with a popular support vector regression (SVR) modeling method, the proposed method for the adaptive dynamic model of the turbofan engine is validated within a supersonic cruise envelops. The results show that the proposed method has not only much hi

论文类型：期刊论文

卷号：6

页面范围：45755-45761

ISSN号：2169-3536

是否译文：否

发表时间：2018-09-01

收录刊物：SCIE

合写作者：李永进,胡忠志

通讯作者：张海波