基于增强型BP网络的气象传感器标校方法

Calibration method of meteorological sensor based on enhanced BP network

针对气象传感器标校过程中测量精度低和生产成本高的问题,将人工智能技术与单片机技术相结合,提出一种气象传感器智能标校方法。该方法将BP神经网络、高斯函数和Levenberg-Marquardt算法相融合,设计一种用于传感器标校的增强型BP网络模型。并将训练好的标校模型移植到单片机中,通过分段多项式来拟合高斯函数,有效减少单片机的计算资源、缩短计算时间。实验结果表明:传统BP网络使气压传感器均方根误差由最初的5.93降低到2.83,减少52.28%的测量误差;而增强型BP网络则使均方根误差降低到0.77,进一步减少34.74%的测量误差。通过分段多项式来拟合高斯函数,显著降低标校模型的计算量,可满足气象探测过程中的时间要求。

Aiming at the problems of low measurement accuracy and high production cost in the process of meteorological sensor calibration,this paper proposes an intelligent calibration method of meteorological sensor by combining artificial intelligence technology and microcontrollers technology.By combining back propagation(BP)neural network,Gaussian function and Levenberg-Marquardt(LM)algorithm,an enhanced BP network is realized for sensor calibration.The calibration model is transplanted to microcontroller unit(MCU).The Gaussian function is fitted by piecewise polynomials,which effectively reduces the computing resources and time of MCU.The experimental result show that:The traditional BP network reduces the mean squared error(MSE)of the atmospheric pressure sensor from 5.93 to 2.83,which reduces the measurement error by 52.28%.The enhanced BP network reduces the MSE to 0.77,which further reduces the measurement error by 34.74%.By fitting the Gaussian function with piecewise polynomial,the complexity of calibration model is significantly reduced,and the time requirement in the process of meteorological observation is met.