基于改进生成对抗网络的国产民机航材消耗预测方法

In order to solve the problems of many factors affecting spare parts consumption forecast, complex time correlation, and few actual consumption samples, in view of the difficulty of capturing the gradual dependence of small sample spare parts consumption data in time sequence with existing methods, a spare parts consumption prediction method based on the improved generation adversarial network generative model and the recurrent neural network prediction model is proposed. This method combines the supervised training of autoregressive learning with the unsupervised training of adversarial learning, and further explores the potential static distribution and temporal step-by-step dependence distribution of the spare parts consumption data. Samples that are closer to the real data are generated to achieve the purpose of expanding the sample volume and improving the forecast accuracy of spare parts consumption. Taking the consumption data of a certain type of domestic civil aircraft as an example, the generation performance and forecast accuracy of the model are evaluated by using the dimensionality reduction visualization method and the forecast score. Compared with other methods, the average absolute error of the predicted value presented by the proposed method is reduced by 3%, which verifies the effectiveness of the proposed method in solving the problem of spare parts prediction.