It is difficult to express the mathematical relationship between the key parameters of suspension and the optimization objectives with intuitive mathematical expressions in the multi-body dynamics model of vehicle-track coupling, the RBFNN surrogate model between suspension key parameters and optimization objectives is built by MATLAB, and R2 is used as an index to evaluate the accuracy of the surrogate model. By using the K-means Clustering Method, when the number of clusters is 400, the R2 values of the training set and the prediction set are greater than 0.9, the results show that the fitting effect of the surrogate model is good, and the accuracy meets the requirements.
References
[1]
Yang, C.L., Huang, Y.H. and Ding, J. J. (2021) Influence of Geometric Parameters of Heavy Haul Track Curve on Wheel-Rail Coupling Dynamic Characteristics. Journal of Transportation Engineering, 21, 215-227.
[2]
Ma, H., Zhang, J., Zhang, J., Jin, T.T. and Song, C.Y. (2020) Influence of Full-Life Cycle Wheel Profile on the Contact Performance of Wheel and Standard Fixed Frog in Heavy Haul Railway. Shock and Vibration, 2020, 1-12. https://doi.org/10.1155/2020/8866692
[3]
Li, J., Zuo, W., E, J., Zhang, Y., Li, Q., Sun, K., et al. (2022) Multi-Objective Optimization of Mini U-Channel Cold Plate with SiO2 Nanofluid by RSM and NSGA-II. Energy, 242, Article ID: 123039. https://doi.org/10.1016/j.energy.2021.123039
[4]
Gribov, A. and Krivoruchko, K. (2020) Empirical Bayesian Kriging Implementation and Usage. Science of The Total Environment, 722, Article ID: 137290. https://doi.org/10.1016/j.scitotenv.2020.137290
[5]
Jie, H., Zheng, G., Zou, J., Xin, X. and Guo, L. (2020) Speed Regulation Based on Adaptive Control and RBFNN for PMSM Considering Parametric Uncertainty and Load Fluctuation. IEEE Access, 8, 190147-190159. https://doi.org/10.1109/access.2020.3031969
[6]
Sun, Y., Ding, S., Zhang, Z. and Jia, W. (2021) An Improved Grid Search Algorithm to Optimize SVR for Prediction. Soft Computing, 25, 5633-5644. https://doi.org/10.1007/s00500-020-05560-w
[7]
Hariri-Ardebili, M., Mahdavi, G., Abdollahi, A. and Amini, A. (2021) An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams. Water, 13, Article 302. https://doi.org/10.3390/w13030302
[8]
Chen, J., Ji, X., Dey, et al. (2022) Intelligent Recognition of Pavement Distress Using Deep Convolutional Neural Network. Journal of Fuzhou University (Natural Science Edition), 50, 530-536.
