Paper Title:
GA-BP Neural Network Based Tire Noise Prediction
  Abstract

According to the complexity and the highly nonlinear characteristics of the tire sound, various parameters affecting tire noise were analyzed. By employing neural network a new method of tire noise prediction was proposed. Combining BP neural networks with genetic algorithms the noise prediction model was set up. In order to effectively predict tire noise, the neural network structure was designed and the input and output parameters of the network were determined. The genetic algorithm was added to the BP network in order to optimize initial weights and search out the optimal solution of the network. Applying laboratory drum method large amounts of tire noise test samples were obtained to train the BP network. Trained neural network can accurately predict tire noise in range of typical frequency bands. The results show that precision of this method is sufficient and the prediction effect is better.

  Info
Periodical
Advanced Materials Research (Volumes 443-444)
Chapter
Chapter 1: Advances in Manufacturing Engineering Techniques and Materials Science
Edited by
Li Jian
Pages
65-70
DOI
10.4028/www.scientific.net/AMR.443-444.65
Citation
Y. Che, W. X. Xiao, L. J. Chen, Z. C. Huang, "GA-BP Neural Network Based Tire Noise Prediction", Advanced Materials Research, Vols. 443-444, pp. 65-70, 2012
Online since
January 2012
Export
Price
$32.00
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