Abstract: Under the combined action of inflation pressure, vertical load and wheel velocity, the lateral forces of 18 working conditions are measured by the analytic system of tire contact pressure on ground. And the complex relations between them are analyzed qualitatively, which they are the complicated nonlinear implicit function. For samples, 558 test dates are selected and used as the network characteristic parameters, an agent-neural network for kind identification is trained and constructed. Because the increasing of network nodes and layers and the superposition principle of nonlinear transfer function, the identification rate is low and node positions are uncertainty in networks. In order to solve these problems，the agent is introduced to optimize the training process of the artificial neural networks. Simulation results showed that the mean error achieves 1.5%. According to the analysis of simulation results compared with magic formula, adaptive model based on agent has the advantage of numerical approximation the for test curve. This method can provide support for car safety design.

Key words: Agent, Lateral-slip characteristics, Neural net, Tire