Development of technology predicting based on EEMD-GRU: An empirical study of aircraft assembly technology

Abstract

Technology prediction has been the subject of many prior studies, which have the issues of the long-term effectiveness, the high uncertainty, and the low predictive accuracy. To address these problems, this study developed a model based on a mixed neural network that combines the Ensemble Empirical Mode Decomposition (EEMD) signal decomposition method with the Gated Recurrent Unit (GRU) deep learning model. In this study, the literature data is first preprocessed using Latent Dirichlet Allocation (LDA) topic modeling, and clusters of key technology topics are obtained accordingly. Secondly, within the identified technology topics, the EEMD signal processing method is employed to decompose complex time-series data into simpler subsequences, and GRU prediction models are established. Thirdly, the ultimate technological prediction results are obtained by integrating each subsequence's prediction results. In addition, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used to evaluate the prediction results. Finally, the field of aircraft assembly technology is analyzed as a case study. The results show that the EEMD-GRU hybrid model excels in prediction accuracy, and brings a new perspective and method to the field of technological prediction.