Modeling water treatment process using fuzzy neural network based on subtractive clustering

Li Wang; Jie Shen

doi:10.1109/CHICC.2008.4605602

Modeling water treatment process using fuzzy neural network based on subtractive clustering

Li Wang, Jie Shen

COLLEGE OF ELECTRICAL ENGINEERING AND CONTROL SCIENCE

Nanjing University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Because of nonlinear, time-varying and time-delaying property, it's difficult to model water treatment process by traditional method, so a Takagi-Sugeno fuzzy model based on subtractive clustering algorithm is proposed in this paper. Firstly, subtractive clustering is used to partition the input space and to determine the initial values of premise parameters and fuzzy rules. Moreover, an improved hybrid study algorithm consisting of a back propagation algorithm and least square algorithm is implemented to optimize the parameters. Finally, this proposed method is used to model the water treatment process, and the simulation results show that it offers the advantages of high precision, fast convergence and fast computing speed.

Original language	English
Title of host publication	Proceedings of the 27th Chinese Control Conference, CCC
Pages	324-328
Number of pages	5
DOIs	https://doi.org/10.1109/CHICC.2008.4605602
State	Published - 2008
Event	27th Chinese Control Conference, CCC - Kunming, Yunnan, China Duration: 16 Jul 2008 → 18 Jul 2008

Publication series

Name	Proceedings of the 27th Chinese Control Conference, CCC

Conference

Conference	27th Chinese Control Conference, CCC
Country/Territory	China
City	Kunming, Yunnan
Period	16/07/08 → 18/07/08

Keywords

Hybrid study algorithm
Subtractive clustering
T-S fuzzy model
Water treatment

Access to Document

10.1109/CHICC.2008.4605602

Cite this

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title = "Modeling water treatment process using fuzzy neural network based on subtractive clustering",

abstract = "Because of nonlinear, time-varying and time-delaying property, it's difficult to model water treatment process by traditional method, so a Takagi-Sugeno fuzzy model based on subtractive clustering algorithm is proposed in this paper. Firstly, subtractive clustering is used to partition the input space and to determine the initial values of premise parameters and fuzzy rules. Moreover, an improved hybrid study algorithm consisting of a back propagation algorithm and least square algorithm is implemented to optimize the parameters. Finally, this proposed method is used to model the water treatment process, and the simulation results show that it offers the advantages of high precision, fast convergence and fast computing speed.",

keywords = "Hybrid study algorithm, Subtractive clustering, T-S fuzzy model, Water treatment",

author = "Li Wang and Jie Shen",

year = "2008",

doi = "10.1109/CHICC.2008.4605602",

language = "英语",

isbn = "9787900719706",

series = "Proceedings of the 27th Chinese Control Conference, CCC",

pages = "324--328",

booktitle = "Proceedings of the 27th Chinese Control Conference, CCC",

note = "27th Chinese Control Conference, CCC ; Conference date: 16-07-2008 Through 18-07-2008",

}

Wang, L & Shen, J 2008, Modeling water treatment process using fuzzy neural network based on subtractive clustering. in Proceedings of the 27th Chinese Control Conference, CCC., 4605602, Proceedings of the 27th Chinese Control Conference, CCC, pp. 324-328, 27th Chinese Control Conference, CCC, Kunming, Yunnan, China, 16/07/08. https://doi.org/10.1109/CHICC.2008.4605602

TY - GEN

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AU - Wang, Li

AU - Shen, Jie

PY - 2008

Y1 - 2008

N2 - Because of nonlinear, time-varying and time-delaying property, it's difficult to model water treatment process by traditional method, so a Takagi-Sugeno fuzzy model based on subtractive clustering algorithm is proposed in this paper. Firstly, subtractive clustering is used to partition the input space and to determine the initial values of premise parameters and fuzzy rules. Moreover, an improved hybrid study algorithm consisting of a back propagation algorithm and least square algorithm is implemented to optimize the parameters. Finally, this proposed method is used to model the water treatment process, and the simulation results show that it offers the advantages of high precision, fast convergence and fast computing speed.

AB - Because of nonlinear, time-varying and time-delaying property, it's difficult to model water treatment process by traditional method, so a Takagi-Sugeno fuzzy model based on subtractive clustering algorithm is proposed in this paper. Firstly, subtractive clustering is used to partition the input space and to determine the initial values of premise parameters and fuzzy rules. Moreover, an improved hybrid study algorithm consisting of a back propagation algorithm and least square algorithm is implemented to optimize the parameters. Finally, this proposed method is used to model the water treatment process, and the simulation results show that it offers the advantages of high precision, fast convergence and fast computing speed.

KW - Hybrid study algorithm

KW - Subtractive clustering

KW - T-S fuzzy model

KW - Water treatment

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U2 - 10.1109/CHICC.2008.4605602

DO - 10.1109/CHICC.2008.4605602

M3 - 会议稿件

AN - SCOPUS:52449122689

SN - 9787900719706

T3 - Proceedings of the 27th Chinese Control Conference, CCC

SP - 324

EP - 328

BT - Proceedings of the 27th Chinese Control Conference, CCC

T2 - 27th Chinese Control Conference, CCC

Y2 - 16 July 2008 through 18 July 2008

ER -

Modeling water treatment process using fuzzy neural network based on subtractive clustering

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