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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/26980
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/26980

    Title: Using feedforward neutral networks and forward selection of input variables for an ergonomics data classification problem
    Authors: David B. Kaber;Patrick G. Dempsey;陳春龍
    Chen, Chun-Lung
    Date: 2004-01
    Issue Date: 2009-01-17 15:59:13 (UTC+8)
    Abstract: A method was developed to accurately predict the risk of injuries in industrial jobs based on datasets not meeting the assumptions of parametric statistical tools, or being incomplete. Previous research used a backward-elimination process for feedforward neural network (FNN) input variable selection. Simulated annealing (SA) was used as a local search method in conjunction with a conjugate-gradient algorithm to develop an FNN. This article presents an incremental step in the use of FNNs for ergonomics analyses, specifically the use of forward selection of input variables. Advantages to this approach include enhancing the effectiveness of the use of neural networks when observations are missing from ergonomics datasets, and preventing overspecification or overfitting of an FNN to training data. Classification performance across two methods involving the use of SA combined with either forward selection or backward elimination of input variables was comparable for complete datasets, and the forward-selection approach produced results superior to previously used methods of FNN development, including the error back-propagation algorithm, when dealing with incomplete data.
    Relation: Human Factors in Ergonomics & Manufacturing, 14(1), 31-49
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1002/hfm.10052
    DOI: 10.1002/hfm.10052
    Appears in Collections:[資訊管理學系] 期刊論文

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