[1]陈宇,许莉薇,黄仲洋,等.SADE-ELM电容层析成像流型辨识算法[J].哈尔滨理工大学学报,2014,(06):32-37.
 CHEN Yu,XU Li-wei,HUANG Zhong-yang,et al.A Self一adaptive Different Evolution Extreme Learning Algorithm for Electrical Capacitance Tomography System[J].哈尔滨理工大学学报,2014,(06):32-37.
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SADE-ELM电容层析成像流型辨识算法()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2014年06期
页码:
32-37
栏目:
计算机与控制工程
出版日期:
2014-12-25

文章信息/Info

Title:
A Self一adaptive Different Evolution Extreme Learning Algorithm for Electrical Capacitance Tomography System
文章编号:
1007一2683(2014)06一0032一06
作者:
陈宇许莉薇黄仲洋江露
(东北林业大学信息与计算机工程学院,黑龙江哈尔滨150040)
Author(s):
CHEN YuXU Li-weiHUANG Zhong-yangJIANG Lu
(School of Information and Computer Science, Northeast I’orestrv University, Harbin 150040,China)
关键词:
电容层析成像自适应差分演化优化极端学习机算法辨识
Keywords:
electrical capacitance tomographySelf adaptive Different evolution extreme learning algorithmidentification
分类号:
TP181
文献标志码:
A
摘要:
针对电容层析成像反问题流型识别较难的问题,提出了一种新的ECT流型辨识算法—差分演化优化极端学习机算法,进而提出了基于自适应差分演化优化极端学习机(SaDE -EML)的ECT辨识算法.在论述极端学习机算法的基拙上,结合差分演化算法对极端学习机算法进行优化,自适应差分演化算法中的关键参数,通过训练得到各类流型的分类器的参数,构造分类器
进行精准与快速分类.实验结果表明:该算法能有效克服极端学习机算法的缺点并提高了局部与全局收敛能力,通过与BP, SVM算法比较,该算法具有竞争力,并为电容层析成像流型辨识的研究提供了新算法.
Abstract:
In order to deal with the difficult flow pattern identification more problem in electrical capacitancetomography(ECT),this papar puts forward a new algorithm-different evolution extreme learning machine algorithm,and a self adaptive extreme learning machine algorithm based on different evolution algorithm(SaDE-ELM )to improve the classical extreme learning machine algorithm. On the basis of the principle extreme learning machinealgorithm,the different evolution algorithm should be united with extreme learning machine algorithm to improve theefficiency of algorithm,then get the parameters through training of samples,establish a classifier to achieve the goalof faster classification. The experimental result shows that the algorithm can efficiently overcome the shortcomings ofextreme learning machine algorithm and has better ability with local and global convergence. By compared with neural network and support vector machine,the algorithm has a certain competitiveness and provides a new algorithmfor the research on the electrical capacitance identification.

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备注/Memo

备注/Memo:
国家948项目(2011一 4一 04};中央高校基本科研业务费专项资金(DL12CB02);黑龙江省教育厅科学技术研究项目(12513016);
黑龙江省博士后基金;黑龙江省自然科学基金(R201347);哈尔滨市科技创新人才专项资金(2013RFQXJ100) .
更新日期/Last Update: 2015-06-03