关键词: 过程神经网络, 航空发动机, 性能参数, 预测, Levenberg-Marquardt学习算法, 相空间重构

Abstract: It was difficult for the traditional methods to predict performance parameters effectively, aiming at this problem, a performance parameter prediction method based on the process neural network was proposed. Back Propagation (BP) learning algorithm was with low convergence speed and it was easy to a local minimum point. To solve these problems, a Levenberg-Marquardt learning algorithm based on the expansion of the orthogonal basis functions was developed. To improve the generalization capability of process neural network, from the quality and scale of the training samples, data pretreatment for the actual measurement data was studied, and a method for the construction of the training samples based on the spline functions approximation and the phase space reconstruction theory was proposed. Finally, the proposed prediction method was applied to predict the performance parameters of aeroengine, and the test results were satisfactory.

Key words: process neural network, aeroengine, performance parameters, prediction, Levenberg-Marquardt learning algorithm, phase space reconstruction