摘要
针对电容层析成像系统ECT(electrical capacitance tomography)流型辨识问题,在对ECT系统工作原理和流型的辨识方法分析的基础上,提出了一种基于Elman神经网络的ECT系统流型辨识方法,该方法通过对ECT系统采集的电容值特征值提取与处理,将提取的特征值作为Elman神经网络的输入进行训练,经训练后达到流型辨识的目的.经仿真实验验证,与传统的BP神经网络相比,该方法具有结构简单,收敛速度快,不会因阶次未知而出现网络结构膨胀的问题,为ECT系统流型辨识的提供一种的有效方法.
To solve the problem of electrical capacitance tomography （ECT） system of flow regime identification,this paper presents a new method based on Elman neural network for ECT system of flow pattern identification,which is on the basis of the work principle of ECT system and the method of flow pattern identification.This method uses part of capacitance to extract its feature,and uses the extracted data to train the Elman neural network to achieve the aim of recognize the type of the flow.After simulation,the result shows that this method is better than BP neural network.This method has a simple structure and a fast convergence speed,at the same time; it does not have the network expansion problem of the unknown order.It is an effective method for ECT system of flow pattern identification.
出处
《哈尔滨理工大学学报》
CAS
2014年第5期103-108,共6页
Journal of Harbin University of Science and Technology
基金
国家自然科学基金(60572153
60972127)
高等学校博士学科点专项科研基金(200802140001)
黑龙江省自然科学基金(QC2012C059)
黑龙江省教育厅计划项目(11541040
12511097)
关键词
电容层析成像
流型辨识
ELMAN神经网络
收敛速度
electrical capacitance tomography
flow regime identification
Elman neural network
convergence speed
作者简介
宋蕾（1989-）,女,硕士研究生;
陈德运（1962-）,男,教授,博士研究生导师;E-mail：chengdeyun@hrbust.edu.cn.
姚玉梅（1989-）,女,硕士研究生.
