MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL
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...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.02.011 |
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| _version_ | 1841535845067128832 |
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| author | XIAO Qian LUO Chao OUYANG ZhiXu CHANG Chao LUO JiaWen |
| author_facet | XIAO Qian LUO Chao OUYANG ZhiXu CHANG Chao LUO JiaWen |
| author_sort | XIAO Qian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-32ff5221b6e24609bb5ba913ce399349 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-32ff5221b6e24609bb5ba913ce3993492025-01-15T02:26:16ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-014331932630610291MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODELXIAO QianLUO ChaoOUYANG ZhiXuCHANG ChaoLUO JiaWenThe 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.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.02.011High speed trainAgent modelRadial Basis Function（RBF）Multi-objective optimizationSensitivity analysis |
| spellingShingle | XIAO Qian LUO Chao OUYANG ZhiXu CHANG Chao LUO JiaWen MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL Jixie qiangdu High speed train Agent model Radial Basis Function（RBF） Multi-objective optimization Sensitivity analysis |
| title | MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL |
| title_full | MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL |
| title_fullStr | MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL |
| title_full_unstemmed | MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL |
| title_short | MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL |
| title_sort | multi objective optimization of vehicle track parameters based on rbf neural network surrogate model |
| topic | High speed train Agent model Radial Basis Function（RBF） Multi-objective optimization Sensitivity analysis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.02.011 |
| work_keys_str_mv | AT xiaoqian multiobjectiveoptimizationofvehicletrackparametersbasedonrbfneuralnetworksurrogatemodel AT luochao multiobjectiveoptimizationofvehicletrackparametersbasedonrbfneuralnetworksurrogatemodel AT ouyangzhixu multiobjectiveoptimizationofvehicletrackparametersbasedonrbfneuralnetworksurrogatemodel AT changchao multiobjectiveoptimizationofvehicletrackparametersbasedonrbfneuralnetworksurrogatemodel AT luojiawen multiobjectiveoptimizationofvehicletrackparametersbasedonrbfneuralnetworksurrogatemodel |