基于Elman神经网络的固相质量流量测量  

Solid Phase Mass Flow Measurement Based on Elman Neural Network

作　　者：张怡 刘洋 ZHANG Yi;LIU Yang(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China)

机构地区：[1]华北理工大学电气工程学院,河北唐山063210

出　　处：《仪表技术与传感器》2020年第8期104-107,共4页Instrument Technique and Sensor

基　　金：国家自然科学基金项目(61803154,61803230);山东省高等学校科学技术计划项目(J18KA330)。

摘　　要：在煤粉的输送过程中,采用双弯管法测量固相质量流量的相关系数时受到外部复杂因素的干扰,存在着很多非线性的环节。Elman神经网络对系统存在的非线性环节具有优势,通过建立的3层Elman神经网络模型,训练得出了与真实值吻合度高的估算值,最大相对误差不超过2%,为煤粉气力输送过程中测量煤粉颗粒的质量流量提供有效的方法。In the process of conveying pulverized coal,the correlation coefficient of solid mass flow measured by double bend pipe method is interfered by external complex factors,and there are many nonlinear links.Elman neural network has advantages over the nonlinear links existing in the system.Through the establishment of a three-layer Elman neural network model,the estimated value with a high consistency with the real value is trained.The maximum error is less than 2%.Measuring the mass flow of pulverized coal during the pneumatic conveying process of pulverized coal provides an efficient method.

关 键 词：双弯管法 固相质量流量 ELMAN神经网络 弯管流量计 非线性环节 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]