Hybrid Deep LSTM-GAT Network With Mechanism Information for Prediction of Mach Number

Abstract

The integration of mechanism knowledge and data-driven technology to establish a deep network for predicting Mach numbers in wind tunnel systems has engineering and scientific significance. An intelligent hybrid model is first established to predict the Mach number in a wind tunnel system with input backlash and noise. A recursive least squares (RLSs) regression modeling strategy is constructed to obtain a regression model. The RLS can describe the main dynamic response between input and output signals. The backlash may reduce the excitation of the input signal, which will be reflected in the output signal and result in output prediction error and parameter estimation deviation. Based on the residual neural network (ResNet) structure, the long short-term memory (LSTM) network with excellent time series prediction performance and the graph attention networks (GATs) with strong non-Euclidean feature extraction ability are, therefore, deeply fused and adopted to estimate the error of the identification model. A sliding window is designed to divide the data and enhance the correlation of adjacent time series error data. A residual block composed of the LSTM network and convolution layer is designed to extract local features. By transforming the data into the topology structure, the GAT is used to capture the non-Euclidean features of the data, and the attention mechanism is adopted to achieve effective data feature updating. Finally, the results of industrial real wind tunnel experiments confirm the effectiveness and practicability of the proposed hybrid modeling algorithm.