Artificial Neural Network versus Similarity Method for Tire Model

Chen Huang; Chen Long; Hao Bin Jiang

doi:10.4028/www.scientific.net/KEM.464.487

Paper Titles

Study on the Formability of Square Box Deep Drawing of Tailor Rolling Blanks
p.469

The Effect of Heat Treatment on the Microstructure of Electroless Ni–P Coatings
p.474

Experiment of Laser Shock Processing on AISI 8620 Alloy Steel
p.478

Soft Sensor Modeling of Biological Fermentation Process Based on tPSO-FNN
p.482

Artificial Neural Network versus Similarity Method for Tire Model
p.487

Friction Behaviors of SiC Particle-Reinforced Cu Matrix Composites
p.492

Research on the Surface Roughness of Dry Cutting Different Levels of Austempering Ductile Iron (ADI)
p.496

Finger Milling-Cutter NC Generating Spiral Bevel Gear Tooth Surfaces & its Machining Simulation
p.501

Experimental Study of Micro-Scale Laser Shock Peening on Copper Foils
p.506

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Artificial Neural Network versus Similarity Method for Tire Model

Abstract:

Traditionally, similarity method is used to predict the tire forces and moments at the tire/road interface. Because of advances in computer science, students have had an alternative method, the artificial neural net works, to the similarity method. Based on data from a set of experimental available data, we compared the prediction of the similarity model and the neural network model by genetic algorithm. The latter showed a slightly (about 9%, 18%) but significantly better performance. The accuracy of both approaches was low at the population level, but both models may be used to estimate longitudinal and lateral tire tread and sidewall deflections during braking, traction and cornering.