基于BP神经网络的雨滴谱仪设计
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摘要
为了减小传统雨滴谱观测仪器的体积、重量、成本,并提高雨滴谱的测量精度,提出一种基于等距导电圆环结构的新型雨滴谱探头、Cortex-M3 ARM处理器及高精度低噪声测量电路的雨滴谱仪。雨滴谱仪利用不同直径雨滴引起输出电压的差异进行测量。以Sigmoid函数作为传递函数,利用梯度下降法建立雨滴谱仪的输出电压和雨滴直径的BP神经网络模型,并将基于该模型的算法嵌入ARM处理器,求出雨滴直径。该雨滴谱仪实现了对直径为2.67～5.56 mm雨滴的观测,测量误差小于±0.1 mm。
In order to reduce the size, weight, and cost of the traditional raindrop spectrometer, and improve the measurement accuracy of the raindrop spectrum,a raindrop spectrometer composed of new raindrop spectrum probe,Cortex-M3 ARM processor,and high-precision and low-noise measurement circuit is proposed,which is based on isometric conductive ring structure. The output voltage difference caused by different raindrop diameter is used in the raindrop spectrometer for measurement. The Sigmoid function is taken as the transfer function,and the gradient descent method is used to establish the BP neural network model of output voltage and raindrop diameter of the raindrop spectrometer. The algorithm based on this model is embedded into the ARM processor to obtain the raindrop diameter. The raindrop spectrometer can realize the observation of raindrop whose diameter within 2.67～5.56 mm,and the measurement error is less than ± 0.1 mm.
In order to reduce the size, weight, and cost of the traditional raindrop spectrometer, and improve the measurement accuracy of the raindrop spectrum,a raindrop spectrometer composed of new raindrop spectrum probe,Cortex-M3 ARM processor,and high-precision and low-noise measurement circuit is proposed,which is based on isometric conductive ring structure. The output voltage difference caused by different raindrop diameter is used in the raindrop spectrometer for measurement. The Sigmoid function is taken as the transfer function,and the gradient descent method is used to establish the BP neural network model of output voltage and raindrop diameter of the raindrop spectrometer. The algorithm based on this model is embedded into the ARM processor to obtain the raindrop diameter. The raindrop spectrometer can realize the observation of raindrop whose diameter within 2.67～5.56 mm,and the measurement error is less than ± 0.1 mm.
引文
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[3]盛世博,王瑄,盛思远,等.沈阳地区天然降雨雨滴特征对坡面产沙量的影响[J].水土保持研究,2017,24(2):12-16.SHENG Shibo,WANG Xuan,SHENG Siyuan,et al. Influence of natural rainfall on slope sediment yield in Shenyang region[J]. Research of soil and water conservation,2017,24(2):12-16.
[4]朱亚乔,刘元波.地面雨滴谱观测技术及特征研究进展[J].地球科学进展,2013,28(6):685-693.ZHU Yaqiao,LIU Yuanbo. Advances in measurement techniques and statistics features of surface raindrop size distribution[J]. Advances in earth science,2013,28(6):685-693.
[5]胡子浩,濮江平,张欢,等.Parsivel激光雨滴谱仪观测较强降水的可行性分析和建议[J].气象科学,2014,34(1):25-31.HU Zihao,PU Jiangping,ZHANG Huan,et al. Feasibility analyses and recommendations of parsiveloptical distrometer in measurements of strong precipitation[J]. Journal of the meteorological sciences,2014,34(1):25-31.
[6] ALSHAWABKEH A N,SHEAHAN T C,WU X. Coupling of electrochemical and mechanical processes in soils under DC fields[J]. Mechanics of materials,2004,36(56):453-465.
[7]焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.JIAO Licheng, YANG Shuyuan, LIU Fang, et al. Seventy years beyond neural networks:retrospect and prospect[J]. Chinese journal of computers,2016,39(8):1697-1716.
[8]罗云芳.一种BP神经网络校正算法的实验室智能温控系统研究[J].现代电子技术,2015,38(20):84-91.LUO Yunfang. Research on laboratory′s intelligent temperature control system based on BP network correction algorithm[J].Modern electronics technique,2015,38(20):84-91.
[9]王蒙,常胜,王豪.一种自适应训练的BP神经网络FPGA设计[J].现代电子技术,2016,39(15):115-118.WANG Meng,CHANG Sheng,WANG Hao. FPGA based design of BP neural network with adaptive training[J]. Modern electronics technique,2016,39(15):115-118.
[10]舒小健,高太长,刘西川,等.基于降水为物理特征测量仪的雨滴形状观测与分析[J].气象,2017,43(1):91-100.SHU Xiaojian,GAO Taichang,LIU Xichuan,et al. Observation and analysis of raindrop shape based on the precipitation micro-physical characteristics sensor[J]. Meteorological,2017,43(1):91-100.