| Autor: |
XIAO Qian, LUO Chao, OUYANG ZhiXu, CHANG Chao, LUO JiaWen |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Jixie qiangdu, Vol 43, Pp 319-326 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1001-9669 |
| DOI: |
10.16579/j.issn.1001.9669.2021.02.011 |
| Popis: |
The RBF（ Radial Basis Function） neural network surrogate model that employed to explore the multi-objective optimization problems of vehicle and track parameters is to improve the dynamic performance of vehicles. The sensitivity of dynamic performance on vehicle and track parameters was analyzed by constructing a vehicle-track coupling dynamic simulation model of high-speed train and using the UM and Isight joint simulation technology. The eight parameters with the highest sensitivity ratio were used as the design variables,and a surrogate model of RBF neural network was established on the response of the dynamic performance. Then the model was performed to optimize the vehicle/track parameters. The results show that the optimization rate of the optimal solution for the derailment coefficient is 13. 14%,and the optimization rate of the wheel load reduction rate is 14. 63% after the vehicle and track parameters are optimized,which demonstrates that the optimization effect is remarkable,and the dynamic performance of the vehicle has been significantly improved. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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