摘要
针对温度传感元件输出的非线性特性,文章建立了智能传感器数据采集的非线性校正模型,利用函数链神经网络的非线性映射特性快速求取反非线性函数参数,实现了温度校准模型的精确拟合,给出了利用函数链神经网络进行参数计算的方法和步骤,通过气象科学中广泛使用的铂电阻温度元件实验验证了算法的有效性,校正精度达到0.1%。
According to the nonlinear property of the output of temperature sensor,a nonlinear correction model for data acquisition of intelligent sensor is established.By using the nonlinear mapping property of the function chain neural network,the parameters of the anti-nonlinear function are quickly obtained,realizing the accurate fitting of the temperature calibration model.The methods and steps of parameter calculation using functional chain neural network are given out.The experiments on platinum resistance temperature elements are widely used in meteorological science demonstrate the effectiveness of the algorithm,and the correction accuracy reaches 0.1%.
作者
徐桂林
黄彦
孙振庭
Xu Guilin;Huang Yan;Sun Zhenting(Changchun Meteorological Instrument Institute Co.,Ltd,Changchun 130102;Changchun CMII Meteorological Science and Technology Co.,Ltd,Changchun 130102)
出处
《气象水文海洋仪器》
2021年第1期20-22,50,共4页
Meteorological,Hydrological and Marine Instruments
关键词
温度传感器
非线性
校正模型
函数链神经网络
temperature sensor
nonlinear property
correction model
function chain neural network
