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Author:

Qiao Junfei (Qiao Junfei.) (Scholars:乔俊飞) | Jia Yanmei (Jia Yanmei.) | Han Honggui (Han Honggui.) (Scholars:韩红桂)

Indexed by:

CPCI-S EI Scopus

Abstract:

According the problem of difficult to measure online water quality parameters of activated sludge process wastewater treatment system, this paper proposed a new growth Self-Organization Neural Network This network can dynamic generate network nodes and grow to suitable network structure rapidly according to need in the learning process no need to advance set the value the structure and scale. The water quality parameters model of wastewater treatment system based on this network, have more strong adaptive ability, can learning online, network structure is simple, learning velocity rapid, prediction effluent water COD concentration effectively according to input, which proved high effectiveness of this method.

Keyword:

water quality prediction Self-Organization wastewater treatment system modeling Neural Network

Author Community:

  • [ 1 ] [Qiao Junfei]Beijing Univ Technol, Coll Elect & Control Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Jia Yanmei]Beijing Univ Technol, Coll Elect & Control Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Han Honggui]Beijing Univ Technol, Coll Elect & Control Engn, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 乔俊飞

    [Qiao Junfei]Beijing Univ Technol, Coll Elect & Control Engn, Beijing 100124, Peoples R China

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Source :

2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11

Year: 2008

Page: 2585-2588

Language: Chinese

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: 5

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