Prediction Model for Biochemical Oxygen Demand Based on CBR and SVR - 文章详情页

作者：

Yan, A.-J. (Yan, A.-J..) (学者：严爱军) | Ni, P.-F. (Ni, P.-F..) | Yu, Y.-H. (Yu, Y.-H..) | Wang, P. (Wang, P..)

收录：

Scopus PKU CSCD

摘要：

For　the　problem　of　monitoring　biochemical　oxygen　demand　(BOD)　concentration　in　wastewater　treatment　process,　a　case-based　reasoning　(CBR)　prediction　model　based　on　support　vector　regression　machine　(SVR)　is　established　in　this　paper.　This　model　is　composed　of　a　case　retrieval,　a　case　reuse,　a　SVR　revision　and　a　case　retention.　The　SVR　revision　model　is　obtained　using　the　SVR　training　to　revise　the　BOD　concentration　suggested　from　the　traditional　CBR　model.　The　experiment　results　indicate　that　the　fitting　error　of　this　model　is　lower　compared　with　the　support　vector　machine　(SVM),　the　BP　neural　network,　RBF　neural　network　and　the　traditional　CBR　method.　The　application　of　SVR　can　effectively　improve　the　regression　performance　and　the　learning　ability　for　a　traditional　CBR　model.　©　2017,　East　China　University　of　Science　and　Technology.　All　right　reserved.

关键词：

Biochemical oxygen demand; Case revision; Case-based reasoning; Support vector regression

作者机构：

[ 1 ] [Yan, A.-J.]School of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Yan, A.-J.]Beijing Key Laboratory of Computational Intelligence & Intelligent System, Beijing, 100124, China
[ 3 ] [Yan, A.-J.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
[ 4 ] [Ni, P.-F.]School of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Ni, P.-F.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
[ 6 ] [Yu, Y.-H.]School of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Yu, Y.-H.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
[ 8 ] [Wang, P.]School of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
[ 9 ] [Wang, P.]Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
[ 10 ] [Wang, P.]Beijing Laboratory for Urban Mass Transit, Beijing, 100124, China

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