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Contribution to Book
Structure Optimization of Pneumatic Tire Using an Artificial Neural Network
Advances in Neural Networks - ISNN 2004 (2004)
  • Xuchun Ren, Georgia Southern University
  • Zhenhan Yao, Tsinghua University
An application of neural networks to tire optimization designs is presented to alleviate the stress concentration of toe opening. As well known, it is either uncertain or time-consuming to obtain the global optimum solution by using classical local search methods when objective function of optimization is both nonconvex and implicit. In addition, it is infeasible to use local search method based on iteration to optimize tire mechanical property because analysis of tire mechanical responses is involved with material nonlinearity, geometry nonlinearity and boundary nonlinearity. In this paper, a GRNN is constructed to optimize the stress of toe opening by looking at an optimum Young’s modulus and cord direction of tire body rubber-cord composite material layer.
  • Strain energy density,
  • Radial basis function neural network,
  • Generalize regression neural network,
  • Structure optimization,
  • Pneumatic tire,
  • Artificial neural network
Publication Date
August 19, 2004
Fuliang Yin, Jun Wang, and Chengan Guo
Springer International Publishing
LNCS - Lecture Notes in Computer Science
Citation Information
Xuchun Ren and Zhenhan Yao. "Structure Optimization of Pneumatic Tire Using an Artificial Neural Network" Dalian, ChinaAdvances in Neural Networks - ISNN 2004 Vol. 3174 (2004) p. 841 - 847
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