A PSO-RRBF neural network for BOD prediction in sewage treatment - 文章详情页

作者：

Chu, Minghui (Chu, Minghui.) | Li, Wenjing (Li, Wenjing.) | Qiao, Junfei (Qiao, Junfei.) (学者：乔俊飞)

收录：

EI Scopus

摘要：

Aiming　at　the　question　that　the　effluent　biochemical　oxygen　demand　(BOD)　in　sewage　treatment　is　difficult　to　measure　accurately　in　real　time,　a　soft-measurement　model　of　recursive　radial　basis　function　(RRBF)　neural　network　based　on　PSO　algorithm　(PSO-RRBF)　is　proposed　to　predict　the　effluent　BOD.　Firstly,　the　PSO　algorithm　is　put　forward　to　determine　not　only　the　input　variables　but　also　the　structure　of　the　RRBF　effectively.　Secondly,　the　gradient　descent　method　is　used　to　adjust　the　weights,　center　and　width.　Finally,　the　soft-measurement　model　is　applied　to　the　actual　sewage　treatment　process.　The　experimental　results　show　that　the　soft-measurement　model　has　a　more　compact　structure　and　its　accuracy　is　improved　compared　with　other　models.　©　2019　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.

关键词：

作者机构：

[ 1 ] [Chu, Minghui]Faculty of Information Technology, Beijing University of Technology, Beijing; 100024, China
[ 2 ] [Chu, Minghui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 3 ] [Li, Wenjing]Faculty of Information Technology, Beijing University of Technology, Beijing; 100024, China
[ 4 ] [Li, Wenjing]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 5 ] [Qiao, Junfei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100024, China
[ 6 ] [Qiao, Junfei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

通讯作者信息：

[li, wenjing]faculty of information technology, beijing university of technology, beijing; 100024, china;;[li, wenjing]beijing key laboratory of computational intelligence and intelligent system, beijing; 100124, china

电子邮件地址：

wenjing.li@bjut.edu.cn

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