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神经网络在小管径超声波流量测试中的应用
引用本文:陈平,王俏,耿庆军.神经网络在小管径超声波流量测试中的应用[J].计算机工程与应用,2008,44(25):78-80.
作者姓名:陈平  王俏  耿庆军
作者单位:山东理工大学 计算机科学与技术学院,山东 淄博 250049
基金项目:山东理工大学校科研和教改项目 
摘    要:介绍了一种基于神经网络的改进型超声波流量测量系统的研制,该系统针对时差法超声波流量计在小管径应用上时差难以测量的不足,采用动量BP算法,实现了对各种非线性影响因素的补偿,通过参数修正有效地提高了系统的测量精度。与传统时差法超声波流量计相比,该系统稳定性好,精度高,具有良好的应用价值和推广价值。

关 键 词:超声波流量  神经网络  动量BP  小管径  
收稿时间:2007-10-9
修稿时间:2008-3-31  

Application of ultrasonic flow measurement for small diameter pipe based on Neural Network
CHEN Ping,WANG Qiao,GENG Qing-jun.Application of ultrasonic flow measurement for small diameter pipe based on Neural Network[J].Computer Engineering and Applications,2008,44(25):78-80.
Authors:CHEN Ping  WANG Qiao  GENG Qing-jun
Affiliation:School of Computer Science and Technology,Shandong University of Technology,Zibo,Shandong 255049,China
Abstract:Introduce that a design of ameliorate ultrasonic flow measurement system.In order to overcome the insufficiency of the ultrasonic flow meter for small diameter pipe in travel time,an momentum BP arithmetic is used to realize the compensate of variety of non-linearity influence factors.And the accuracy of measurement has been improved by parameter amending.Compared with the traditional ultrasonic flow meter,the system has well stability,precision and practicality.
Keywords:ultrasonic flow  neural network  momentum BP  small diameter pipe
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