基于BP神经网络的超声流量计声道流速修正方法研究
Research on Correction Method of Sound Channel Velocity of Ultrasound Flowmeter Based on BP Neural Network
摘要: 超声波流量计直接测量得到的流速为超声波传播路径上的线平均流速，它与管道截面平均流速不同，为了获得流量的准确值，必须对测量得到的流速进行修正。本文建立了基于BP神经网络的多声道线平均流速修正模型，根据超声波流量计实测实验获得充足的实验数据作为数据样本，对建立的模型进行训练和验证，通过对训练后模型的验证发现，建立的修正模型可以准确修正超声波流量计的声道流速。
Abstract: The flow velocity measured directly by the ultrasonic flowmeter is the line average velocity along the ultrasonic propagation path, and it is different from the average flow velocity of the pipeline section. In order to obtain the exact value of flow, the measured flow velocity must be corrected. In this paper, a velocity correction model of multi-channel based on BP neural network is established. According to the experiment of ultrasonic flowmeter, sufficient experimental data are obtained as data samples to train and valid the model. Through the validation of the model after training, it is found that established model can accurately correct the flow velocity in the channel of the ultrasonic flowmeter.
文章引用：李晶晶, 王建民, 吴晓昱, 杨希文, 李晨, 周齐, 滕梓洁, 史去非. 基于BP神经网络的超声流量计声道流速修正方法研究[J]. 仪器与设备, 2019, 7(3): 186-192. https://doi.org/10.12677/IaE.2019.73025

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