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Author:

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

Indexed by:

CPCI-S

Abstract:

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.

Keyword:

effluent BOD gradient descent method recursive RBF PSO algorithm soft measurement

Author Community:

  • [ 1 ] [Chu, Minghui]Beijing Univ Technol, Fac Informat Technol, Beijing 100024, Peoples R China
  • [ 2 ] [Li, Wenjing]Beijing Univ Technol, Fac Informat Technol, Beijing 100024, Peoples R China
  • [ 3 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100024, Peoples R China
  • [ 4 ] [Chu, Minghui]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 5 ] [Li, Wenjing]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 6 ] [Qiao, Junfei]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Li, Wenjing]Beijing Univ Technol, Fac Informat Technol, Beijing 100024, Peoples R China;;[Li, Wenjing]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC)

ISSN: 2161-2927

Year: 2019

Page: 1593-1597

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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