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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

has been cited by the following article:

• TITLE: Research on C80 Train-Track Coupling Model

  AUTHORS: Lili Liu, Jihong Zuo, Yan Li, Chuanyin Yang, Qingwan Tang, Tianle Kuang

  KEYWORDS: Vehicle-Track Coupling, Suspension Parameters, Surrogate Model

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.13 No.2, March 27, 2025

  ABSTRACT: 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.