【摘要】 针对压力传感器对温度变化和电流波动的交叉灵敏度问题,采用径向基函数(RBF)人工神经网络法对其进行数据融合处理,详细讨论了网络的训练过程和数据融合过程,消除温度和电流对压力传感器的影响。仿真结果表明:当温度变化48.5℃,电流波动3%时,经RBF神经网络数据融合后,压力波动为0.544%,大大降低了交叉干扰,提高传感器的稳定性及其精度,满足在线融合的需要。更多还原

【Abstract】 Aimed at the cross-sensitivity of pressure sensor to temperature flux and the current flux,a data fusion method based on RBF network is proposed for eliminating the influence of two factors on pressure sensor,the training process and data confusion particularly are discussed.The result of simulation shows that for the case of 48.5℃ of temperature flux and 3%of power flux,the pressure fluctuating is 0.544%,the cross-sensitivity of system is reduced,the stability and precision of the sensor are increased,it is satisfied of on-line data fusion.更多还原