Design and application of hybrid gradient descent-based fuzzy neural network - Details

Author：

Han, Hong-Gui (Han, Hong-Gui.) (Scholars：韩红桂) | Lin, Zheng-Lai (Lin, Zheng-Lai.) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (Scholars：乔俊飞)

Indexed by：

EI Scopus PKU CSCD

Abstract：

To　improve　the　convergence　speed　and　generalization　ability　of　the　fuzzy　neural　network　(FNN),　a　fuzzy　neural　network,　based　on　the　hybrid　gradient　(HG)　descent　algorithm,　is　proposed　in　this　paper.　This　HG-FNN　can　obtain　the　adaptive　learning　rate　of　the　parameter　adjustment　process.　Then,　the　chain　rule　is　used　to　calculate　the　gradient　descent　of　the　learning　process　to　adjust　the　parameters　of　FNN.　Meanwhile,　the　convergence　proof　of　HG-FNN　is　given　in　details　to　ensure　the　convergence　speed　and　the　precision　of　FNN.　Finally,　the　proposed　HG-FNN　is　used　to　model　the　nonlinear　systems　and　predict　the　effluent　qualities　of　wastewater　treatment　process.　The　results　show　that　the　proposed　HG-FNN　owns　faster　convergence　speed,　as　well　as　with　suitable　generalization　ability　than　other　FNNs.　©　2017,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.

Keyword：

Nonlinear systems Learning systems Effluent treatment Fuzzy inference Gradient methods Water quality Fuzzy neural networks Fuzzy logic Effluents Wastewater treatment

Author Community：

[ 1 ] [Han, Hong-Gui]College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Han, Hong-Gui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Lin, Zheng-Lai]College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Lin, Zheng-Lai]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Qiao, Jun-Fei]College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Qiao, Jun-Fei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address：

韩红桂
[han, hong-gui]college of electronic information & control engineering, beijing university of technology, beijing; 100124, china;;[han, hong-gui]beijing key laboratory of computational intelligence and intelligent system, beijing university of technology, beijing; 100124, china

Email：

rechardhan@bjut.edu.cn

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