Transactions of the Korean Society of Automotive Engineers (한국자동차공학회논문집)

The Korean Society of Automotive Engineers (한국자동차공학회)
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Optimization of Neural Network Structure for the Efficient Bushing Model

효율적인 신경망 부싱모델을 위한 신경망 구성 최적화

  • Lee, Seung-Kyu (Graduate School of Mechanical Engineering, Pukyong National University) ;
  • Kim, Kwang-Suk (Department of Automotive Engineering, Inha Technical College) ;
  • Sohn, Jeong-Hyun (Department of Mechanical Engineering, Pukyong National University)
  • Published : 2007.09.01
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Abstract

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

Keywords

References

  1. ADAMS User's Guide, MSC Software Corporation, 2003
  2. A. J. Barber, 'Accurate Models for Complex Vehicle Components using Empirical Methods,' SAE 2000-01-1625, 2000
  3. J. H. Sohn, W. S. Yoo and D. W. Park, 'Empirical Bushing Model Using Artificial Neural Network,' Transactions of KSAE, Vol. 11, No.4, pp.151-157, 2003
  4. S. M. Savaresi, S. Bittanti and M. Montiglio, 'Identification of Semi-physical and Black-box Non-linear Models: The Case of MR-dampers for Vehicles Control,' Automatica, Vol.41, pp.113-127, 2005