基于神经网络系统的附着系数计算模型

对几种附着系数计算模型进行了深入研究,在全面分析了主要影响附着系数因素的基础上,采用神经网络优化算法,分别建立了以路面状况、胎压及车速为输入,以附着系数为输出的3种轮胎花纹的神经网络附着系数计算模型,并验证了模型的有效性。该模型能够计算汽车在不同的行驶工况下的轮胎/路面间的附着系数,从而为附着系数实时监控提供理论依据,为行车安全提供保障。

Calculation Model for Adhesion Coefficient Based on Neural Networks System

Three kinds of neural network models for adhesion coefficient, with road condition, tire pressure and motorcar speed as input parameters and adhesion coefficient as output parameter, are constructed respectively in accordance with the main factors affecting adhesion coefficient by means of the neural networks optimization algorithm. The validity of the models is verified. It proves that the models can be applied to calculate the adhesion coefficient when motorcars run under different conditions, which provide a theoretical basis for real-time monitoring of adhesion coefficient and a guarantee of safe driving as well.