| Summary: | 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 higher precision, but also less data storage and better real-time ability than the SVR method.
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