MULTI-OUTPUT REGRESSION PREDICTION OF PNEUMATIC SUBMERGING RESISTANCE AND DISTURBANCE AREA BASED ON NEURAL NETWORK

Abstract

The current field of pneumatic subventing prediction focuses on a single task and neglects the possible interrelationships between different outputs. In order to improve the prediction accuracy and reduce the number of algorithm model establishment, this study conducted field experiments on soil in autumn and winter. Neural network algorithms RBF (radial basis neural network), BP (backward propagation neural network), DNN (Deep learning network) and CNN (Convolutional neural network) were used to make multi-output regression prediction for changing the traction resistance and disturbance area affected by different levels of subsooning velocity, depth and pressure value in the process of pneumatic subsooning. The evaluation indexes RMSE, MAE and R2 were compared with the single output regression model, and the accuracy of the four models with the highest accuracy was compared with that of its own single output model to prove the correlation between traction resistance and disturbance area. The results showed that the R2 of the four model test sets of RBF, BP, DNN and CNN were 0.9999, 0.9966, 0.9986 and 0.9762, respectively. The R2 of the disturbance area are 0.9997, 0.9924, 0.9968 and 0.9715, respectively. RBF has the highest R2 and the lowest RMSE and MAE, indicating that the RBF model has the best prediction effect. Compared with the single output regression model of RBF model, the prediction accuracy of both outputs is higher, so it can be used to predict the subsoiling drag resistance and disturbance area.