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    戚金清

    • 副教授       硕士生导师
    • 性别:男
    • 毕业院校:东京工业大学
    • 学位:博士
    • 所在单位:信息与通信工程学院
    • 学科:通信与信息系统. 信号与信息处理
    • 电子邮箱:jinqing@dlut.edu.cn

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    The study of using an extreme learning machine for rapid concentration estimation in multi-component gas mixtures

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    论文类型:期刊论文

    第一作者:Zhao, Lin

    通讯作者:Qi, JQ (reprint author), Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116023, Peoples R China.

    合写作者:Qi, Jinqing,Wang, Jing,Yao, Pengjun

    发表时间:2012-08-01

    发表刊物:MEASUREMENT SCIENCE AND TECHNOLOGY

    收录刊物:SCIE、EI、Scopus

    卷号:23

    期号:8

    ISSN号:0957-0233

    关键字:sensor array; gas mixtures; concentration estimation; extreme learning machine

    摘要:Cross-sensitivity is one of the major unpleasant characteristics of metal oxide gas sensors. In order to solve this challenging problem, artificial neural networks have proved to be very powerful tools, among which back propagation (BP) and radial basis function (RBF) neural networks are the two most commonly used ones in data analysis of metal oxide gas sensors and arrays. However, relatively long training time is the major disadvantage for the BP and RBF neural networks. In order to solve this problem, an extreme leaning machine (ELM) is introduced and studied in this paper. Experimental results show that ELM networks can achieve 466 and 333 times faster training speed than the BP and RBF neural networks, respectively. In addition, ELM networks can achieve comparable concentration prediction accuracy to RBF networks which is much better than BP networks.