基于RBF神经网络的车辆牵引性能测试数据融合技术

Data fusion technology in vehicle traction performance test based on RBF neural network

针对目前车辆牵引性能测试中,牵引力传感器由于环境温度变化出现测量误差,影响了测试精度的问题,采用基于神经网络的数据融合技术对其进行补偿,不但避免了硬件补偿的复杂性,且提高了测试精度,取得较好的效果。实验证明:采用基于径向基函数(RBF)神经网络的数据融合技术补偿牵引力传感器中由于温度漂移而引起的误差较传统的补偿方式,具有较大的优势,有一定的实用性和推广价值。

In vehicle traction performance test, the traction sensor＇s test has great error because of the change of the condition temperature, sothe data fusion technology based on RBF neural network is adoptd to compensate the error. This method reduces the compensation complexion and improves the accuracy. Relative to other conventional methods, it has more great advantages and is practicable.