Dissolved oxygen control system based on the T-S fuzzy neural network - 文章详情页

作者：

Fu, Wen-Tao (Fu, Wen-Tao.) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (学者：乔俊飞) | Han, Gai-Tang (Han, Gai-Tang.) | Meng, Xi (Meng, Xi.)

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EI Scopus

摘要：

In　this　paper,　a　novel　kind　of　the　dissolved　oxygen　(DO)　concentration　control　system　was　proposed　based　on　the　T-S　fuzzy　neural　network.　The　proposed　T-S　fuzzy　neural　network　controller　was　used　to　control　the　DO　concentration　in　the　Benchmark　Simulation　Model　No.1　(BSM1)　wastewater　treatment　platform.　The　parameters　of　the　neural　network　were　adjusted　online　through　the　error　back　propagation　algorithm　to　get　the　minimum　error.　By　adjusting　the　learning　rate　online,　the　convergence　speed　of　the　system　was　accelerated,　and　then　the　DO　concentration　in　the　wastewater　treatment　system　was　controlled　fast　and　efficiently　in　real-time.　Compared　with　BP　and　PID　controllers　through　the　digital　simulation,　the　results　showed　that　the　control　effect　of　the　DO　concentration　based　on　T-S　fuzzy　neural　network　control　system　was　better.　Besides,　the　test　results　under　three　kinds　of　weather　condition　showed　that　better　adaptability　and　robustness　were　also　gained　in　this　control　system.　©　2015　IEEE.

关键词：

Backpropagation algorithms Biochemical oxygen demand Controllers Dissolution Dissolved oxygen Fuzzy inference Fuzzy logic Fuzzy neural networks Reclamation Simulation platform Three term control systems Wastewater treatment

作者机构：

[ 1 ] [Fu, Wen-Tao]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 2 ] [Qiao, Jun-Fei]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 3 ] [Han, Gai-Tang]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 4 ] [Meng, Xi]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China

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