REFERENCES
1. Na, J.; Huang, Y.; Wu, X.; Liu, Y. J.; Li, Y.; Li, G. Active suspension control of quarter-car system with experimental validation. IEEE. Trans. Syst. Man. Cybern. Syst. 2022, 52, 4714-26.
2. Bai, R.; Wang, H. B. Robust optimal control for the vehicle suspension system with uncertainties. IEEE. Trans. Cybern. 2022, 52, 9263-73.
3. Pan, Y.; Sun, Y.; Li, Z.; Gardoni, P. Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations. Reliab. Eng. Syst. Saf. 2023, 230, 108950.
4. Jiang, X.; Xu, X.; Shi, T.; Atindana, V. A. Nonlinear characteristic analysis of gas-interconnected quasi-zero stiffness pneumatic suspension system: a theoretical and experimental study. Chin. J. Mech. Eng. 2024, 37, 58.
5. Liu, J.; Du, D.; He, J.; Zhang, C. Prediction of remaining useful life of railway tracks based on DMGDCC-GRU hybrid model and transfer learning. IEEE. Trans. Veh. Technol. 2024, 73, 7561-75.
6. Zhang, Y. W.; Zhao, Y.; Zhang, Y. H.; Lin, J. H.; He, X. W. Riding comfort optimization of railway trains based on pseudo-excitation method and symplectic method. J. Sound. Vib. 2013, 332, 5255-70.
7. Bruni, S.; Meijaard, J. P.; Rill, G.; Schwab, A. L. State-of-the-art and challenges of railway and road vehicle dynamics with multibody dynamics approaches. Multibody. Syst. Dyn. 2020, 49, 1-32.
8. Kraft, S.; Puel, G.; Aubry, D.; Fünfśchilling, C. Parameter identification of multi-body railway vehicle models - application of the adjoint state approach. Mech. Syst. Signal. Process. 2016, 80, 517-32.
9. Yang, Y.; Zeng, W.; Qiu, W. S.; Wang, T. Optimization of the suspension parameters of a rail vehicle based on a virtual prototype Kriging surrogate model. Proc. Inst. Mech. Eng. 2016, 230, 1890-8.
10. Chen, G.; Zhang, K.; Xue, X.; et al. A radial basis function surrogate model assisted evolutionary algorithm for high-dimensional expensive optimization problems. Appl. Soft. Comput. 2022, 116, 108353.
11. Ni, P.; Li, J.; Hao, H.; Zhou, H. Reliability based design optimization of bridges considering bridge-vehicle interaction by Kriging surrogate model. Eng. Struct. 2021, 246, 112989.
12. Wei, F. F.; Chen, W. N.; Mao, W.; Hu, X. M.; Zhang, J. An efficient two-stage surrogate-assisted differential evolution for expensive inequality constrained optimization. IEEE. Trans. Syst. Man. Cybern. Syst. 2023, 53, 7769-82.
13. Chen, Q.; Zhang, H.; Zhou, H.; Sun, J.; Tian, Y. Adaptive design of experiments for fault injection testing of highly automated vehicles. IEEE. Intell. Transp. Syst. Mag. 2024, 16, 35-52.
14. Zhang, L.; Li, T.; Zhang, J.; Piao, R. Optimization on the crosswind stability of trains using neural network surrogate model. Chin. J. Mech. Eng. 2021, 34, 86.
15. Zhang, C.; Wang, Y.; He, J. Suspension parameter estimation method for heavy-duty freight trains based on deep learning. Big. Data. Cogn. Comput. 2024, 8, 181.
16. Jiang, R.; Jin, Z.; Liu, D.; Wang, D. Multi-objective lightweight optimization of parameterized suspension components based on NSGA-II algorithm coupling with surrogate model. Machines 2021, 9, 107.
17. Gu, J.; Hua, W.; Yu, W.; Zhang, Z.; Zhang, H. Surrogate model-based multiobjective optimization of high-speed permanent magnet synchronous machine: construction and comparison. IEEE. Trans. Transp. Electr. 2023, 9, 678-88.
18. Liu, C.; Wan, Z.; Liu, Y.; Li, X.; Liu, D. Trust-region based adaptive radial basis function algorithm for global optimization of expensive constrained black-box problems. Appl. Soft. Comput. 2021, 105, 107233.
19. Ye, N.; Long, T.; Shi, R.; Wu, Y. Radial basis function-assisted adaptive differential evolution using cooperative dual-phase sampling for high-dimensional expensive optimization problems. Struct. Multidiscip. Optim. 2022, 65, 241.
20. Yao, Y.; Chen, X.; Li, H.; Li, G. Suspension parameters design for robust and adaptive lateral stability of high-speed train. Veh. Syst. Dyn. 2023, 61, 943-67.
21. Tsattalios, S.; Tsoukalas, I.; Dimas, P.; Kossieris, P.; Efstratiadis, A.; Makropoulos, C. Advancing surrogate-based optimization of time-expensive environmental problems through adaptive multi-model search. Environ. Model. Softw. 2023, 162, 105639.
22. Hua, C.; Jiang, C.; Niu, R.; et al. Double neural networks enhanced global mobility prediction model for unmanned ground vehicles in off-road environments. IEEE. Trans. Veh. Technol. 2024, 73, 7547-60.
23. Tong, H.; Huang, C.; Minku, L. L.; Yao, X. Surrogate models in evolutionary single-objective optimization: a new taxonomy and experimental study. Inform. Sci. 2021, 562, 414-37.
24. Flores, P.; Ambrósio, J.; Lankarani, H. M. Contact-impact events with friction in multibody dynamics: back to basics. Mech. Mach. Theory. 2023, 184, 105305.
25. Millan, P.; Pagaimo, J.; Magalhães, H.; Ambrósio, J. Clearance joints and friction models for the modelling of friction damped railway freight vehicles. Multibody. Syst. Dyn. 2023, 58, 21-45.
26. Zhou, Y.; Mei, T. X.; Freear, S. Real-time modeling of wheel-rail contact laws with system-on-chip. IEEE. Trans. Parallel. Distrib. Syst. 2010, 21, 672-84.
27. Liu, B.; Bruni, S. Comparison of wheel-rail contact models in the context of multibody system simulation: Hertzian versus non-Hertzian. Veh. Syst. Dyn. 2022, 60, 1076-96.
28. Wu, H.; Zeng, X. H.; Lai, J.; Yu, Y. Nonlinear hunting stability of high-speed railway vehicle on a curved track under steady aerodynamic load. Veh. Syst. Dyn. 2020, 58, 175-97.
29. Sun, J.; Meli, E.; Song, X.; Chi, M.; Jiao, W.; Jiang, Y. A novel measuring system for high-speed railway vehicles hunting monitoring able to predict wheelset motion and wheel/rail contact characteristics. Veh. Syst. Dyn. 2023, 61, 1621-43.
30. Chen, X.; Huang, J.; Yi, M. Cost estimation for general aviation aircrafts using regression models and variable importance in projection analysis. J. Clean. Prod. 2020, 256, 120648.
31. Broomhead, D. S.; Lowe, D. Radial basis functions, multi-variable functional interpolation and adaptive networks. 1988. https://apps.dtic.mil/sti/html/tr/ADA196234/. (accessed 2026-03-20).
32. Zheng, W.; Doerr, B. Approximation guarantees for the non-dominated sorting genetic algorithm II (NSGA-II). IEEE. Trans. Evol. Comput. 2024, 891-905.
33. Zhang, D.; Li, C.; Luo, S.; et al. Multi-objective control of residential HVAC loads for balancing the user's comfort with the frequency regulation performance. IEEE. Trans. Smart. Grid. 2022, 13, 3546-57.
34. Ma, Y.; Xiao, Y.; Wang, J.; Zhou, L. Multicriteria optimal Latin hypercube design-based surrogate-assisted design optimization for a permanent-magnet vernier machine. IEEE. Trans. Magn. 2021, 58, 1-5.
35. Yuan, Z.; Kong, L.; Gao, D.; et al. Multi-objective approach to optimize cure process for thick composite based on multi-field coupled model with radial basis function surrogate model. Compos. Commun. 2021, 24, 100671.
36. Li, G.; Yao, Y.; Shen, L.; Deng, X.; Zhong, W. Influence of yaw damper layouts on locomotive lateral dynamics performance: Pareto optimization and parameter analysis. J. Zhejiang. Univ. Sci. A. 2023, 24, 450-64.
37. Zhao, Z.; Chen, W.; Wu, X.; Chen, P. C. Y.; Liu, J. LSTM network: a deep learning approach for short-term traffic forecast. IET. Intell. Trans. Syst. 2017, 11, 68-75.
38. Yao, H. Y.; Wan, W. G.; Li, X. End-to-end pedestrian trajectory forecasting with transformer network. ISPRS. Int. J. Geo. Inf. 2022, 11, 44.
39. Suawa, P.F.; Halbinger, A.; Jongmanns, M.; Reichenbach, M. Noise-robust machine learning models for predictive maintenance applications. IEEE. Sens. J. 2023, 23, 15081-92.
40. Lecuyer, M.; Atlidakis, V.; Geambasu, R.; Hsu, D.; Jana, S. Certified robustness to adversarial examples with differential privacy. In 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, USA, May 19-23, 2019; IEEE, 2019; pp. 656–72.






