Meteorological observation plays an important role in establishing atmospheric theory and improving the accuracy of weather forecasts. Sensors are the foundation and the core in the meteorological observation. Only the accurate calibration of the sensor can ensure the validity of measurement data. In most current methods of sensor calibration, the least squares method is used for calibration which results in low calibration accuracy. In this paper, a sensor calibration model is implemented by using artificial intelligence technology. By combining a back propagation (BP) neural network, a Gaussian function and the Levenberg-Marquardt (LM) algorithm, an enhanced BP network is realized for sensor calibration. The calibration model is transplanted to a Microcontroller unit (MCU). The Gaussian function is fitted by piecewise polynomials, which effectively reduces the computing resources and time use of the MCU. The experimental results show that: the traditional BP network reduces the...

中文翻译：

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基于增强BP网络的气象传感器标定方法

气象观测在建立大气理论和提高天气预报准确性方面起着重要作用。传感器是气象观测的基础和核心。只有传感器的准确校准才能确保测量数据的有效性。在大多数当前的传感器校准方法中，最小二乘法用于校准，这导致较低的校准精度。本文采用人工智能技术实现了传感器标定模型。通过结合反向传播（BP）神经网络，高斯函数和Levenberg-Marquardt（LM）算法，可以实现用于传感器校准的增强型BP网络。校准模型将移植到微控制器单元（MCU）中。高斯函数由分段多项式拟合，有效地减少了MCU的计算资源和时间。实验结果表明：传统的BP网络减少了...