Paper Title:
Multi-Body Model Identification of Vehicle Semi-Active Suspension Based on Genetic Neural Network
  Abstract

A multi-body vehicle dynamics model was established using ADAMS and a multilayer feed forward neural network of series parallel structure was built by Matlab in this study. The weights and threshold of neural networks which has built was optimizes by GA. This method was used in identifying multi-body vehicle dynamics model. The results show that the maximum error of identification is less than 0.05% and the network convergence rapidly. The designed genetic neural network could replace the vehicle semi-active suspension systems using in neural network adaptive control which can avoid the difficulty of establishing accurately mathematical model and the poor effective of traditional identification methods for the vehicle semi-active suspension.

  Info
Periodical
Chapter
Chapter 7: Computer Application in Design and Manufacturing (1)
Edited by
Dongye Sun, Wen-Pei Sung and Ran Chen
Pages
4069-4073
DOI
10.4028/www.scientific.net/AMM.121-126.4069
Citation
J. J. Zhang, B. A. Han, R. Z. Gao, "Multi-Body Model Identification of Vehicle Semi-Active Suspension Based on Genetic Neural Network", Applied Mechanics and Materials, Vols. 121-126, pp. 4069-4073, 2012
Online since
October 2011
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$32.00
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