Transactions of the Korean Society for Noise and Vibration Engineering (한국소음진동공학회논문집)

The Korean Society for Noise and Vibration Engineering (한국소음진동공학회)
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Optimization of Design Variables of a Train Suspension Using Neural Network Model

신경회로망 모델을 이용한 철도 현가장치 설계변수 최적화

  • Published : 2002.07.01
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Abstract

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

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References

  1. 김영국 등, 2001, "바람이 고속전철의 동적 안정성에 미치는 영향 분석", 한국소음진동공학회논문집, 제11권, 제8호, pp. 349-356.
  2. Nick, T.. 1997, "Numerical Methods for Modeling and Optimization of Noise Emission Applications", ASME Symposium in Acoustics and Noise Software, Detroit, MI. USA
  3. Bennet, 1998, "Issues in Industrial Multidisciplinary Optimization", AlAA Paper 98-4737.
  4. 박찬경 등, 2000, "반응표면 모델에 의한 철도차량 대차의 탄성조인트 최적 설계", 대한기계학회논문집 A권, 제24권, 제3호, pp. 661-666.
  5. Chen, S. et al., 1990, "Non-linear System Identification Using Neural Networks", Int. J. Control. Vol. 6, pp. 1191-1214.
  6. 김동진 등, 1997, "신경망을 이용한 냉간단조품의 금형 형상 설계", 대한기계학회 논문집 A권, 제21권, 제5호, pp. 727-734.
  7. 이상배, 1999, 퍼지-뉴로제어 시스템, 교학사.
  8. Hagon, M. T. et al., 1996, Nueral Network Design, Boston, MA:PWS Publishing.
  9. Price, K. et al., 1997, "Differential Evolution: Numerical Optimization Made Easy," Dr. Dobbs Journal. pp. 18-24.
  10. Storn, R., 1996, "Minimizing the Real Functions of the ICEC96 Contest by Differential Evolution," IEEE Conference on Evolutionary Computation, pp. 842-844, Nagoya.
  11. AEA Technology, VAMPIRE Users Manual 1998.
  12. LMS, 1998, OPTIMUS Users Guide.
  13. Bezdek, J C., 1981, Pattern Recognition with Fuzzy Objective Function, Plenum Press, New York.
  14. MacKay, D. J. C., 1992, "Bayesian Interpolation", Neural Computation, Vol. 4, No.3, pp. 415-447. https://doi.org/10.1162/neco.1992.4.3.415

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