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題名 Using feedforward neutral networks and forward selection of input variables for an ergonomics data classification problem
作者 David B. Kaber;Patrick G. Dempsey;陳春龍
Chen, Chun-Lung
日期 2004-01
上傳時間 17-一月-2009 15:59:13 (UTC+8)
摘要 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.
關聯 Human Factors in Ergonomics & Manufacturing, 14(1), 31-49
資料類型 article
DOI http://dx.doi.org/10.1002/hfm.10052
dc.creator (作者) David B. Kaber;Patrick G. Dempsey;陳春龍en_US
dc.creator (作者) Chen, Chun-Lung-
dc.date (日期) 2004-01en_US
dc.date.accessioned 17-一月-2009 15:59:13 (UTC+8)-
dc.date.available 17-一月-2009 15:59:13 (UTC+8)-
dc.date.issued (上傳時間) 17-一月-2009 15:59:13 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/26980-
dc.description.abstract (摘要) 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.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Human Factors in Ergonomics & Manufacturing, 14(1), 31-49en_US
dc.title (題名) Using feedforward neutral networks and forward selection of input variables for an ergonomics data classification problemen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1002/hfm.10052en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1002/hfm.10052en_US