A new fuzzy modeling and identification based on fast-cluster and genetic algorithm

Fucai Liu; Pingli Lu; Run Pei

A new fuzzy modeling and identification based on fast-cluster and genetic algorithm

Fucai Liu^*, Pingli Lu, Run Pei

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

9 Citations (Scopus)

Abstract

A new fuzzy identification algorithm is proposed in this paper, which include five blocks: input variables partition block, fast-cluster block, genetic algorithm block, tuning block and termination block. Fast-cluster block is to identify antecedent parameters of T-S model speedily. Tuning block is to fine tune the parameters of T-S model using the gradient descent approach and termination block checks if the result is satisfactory. The proposed algorithm not only has the advantage of simplicity, but also has high accuracy, strong automation. The simulations indicate that the algorithm is effective in constructing T-S model for complex nonlinear systems.

Original language	English
Pages	290-293
Number of pages	4
Publication status	Published - 2004
Externally published	Yes
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Fast-cluster
Genetic algorithm
Gradient descent
T-S model

Cite this

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title = "A new fuzzy modeling and identification based on fast-cluster and genetic algorithm",

abstract = "A new fuzzy identification algorithm is proposed in this paper, which include five blocks: input variables partition block, fast-cluster block, genetic algorithm block, tuning block and termination block. Fast-cluster block is to identify antecedent parameters of T-S model speedily. Tuning block is to fine tune the parameters of T-S model using the gradient descent approach and termination block checks if the result is satisfactory. The proposed algorithm not only has the advantage of simplicity, but also has high accuracy, strong automation. The simulations indicate that the algorithm is effective in constructing T-S model for complex nonlinear systems.",

keywords = "Fast-cluster, Genetic algorithm, Gradient descent, T-S model",

author = "Fucai Liu and Pingli Lu and Run Pei",

year = "2004",

language = "English",

pages = "290--293",

note = "WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings ; Conference date: 15-06-2004 Through 19-06-2004",

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TY - CONF

T1 - A new fuzzy modeling and identification based on fast-cluster and genetic algorithm

AU - Liu, Fucai

AU - Lu, Pingli

AU - Pei, Run

PY - 2004

Y1 - 2004

N2 - A new fuzzy identification algorithm is proposed in this paper, which include five blocks: input variables partition block, fast-cluster block, genetic algorithm block, tuning block and termination block. Fast-cluster block is to identify antecedent parameters of T-S model speedily. Tuning block is to fine tune the parameters of T-S model using the gradient descent approach and termination block checks if the result is satisfactory. The proposed algorithm not only has the advantage of simplicity, but also has high accuracy, strong automation. The simulations indicate that the algorithm is effective in constructing T-S model for complex nonlinear systems.

AB - A new fuzzy identification algorithm is proposed in this paper, which include five blocks: input variables partition block, fast-cluster block, genetic algorithm block, tuning block and termination block. Fast-cluster block is to identify antecedent parameters of T-S model speedily. Tuning block is to fine tune the parameters of T-S model using the gradient descent approach and termination block checks if the result is satisfactory. The proposed algorithm not only has the advantage of simplicity, but also has high accuracy, strong automation. The simulations indicate that the algorithm is effective in constructing T-S model for complex nonlinear systems.

KW - Fast-cluster

KW - Genetic algorithm

KW - Gradient descent

KW - T-S model

UR - http://www.scopus.com/inward/record.url?scp=4444301913&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:4444301913

SP - 290

EP - 293

T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

A new fuzzy modeling and identification based on fast-cluster and genetic algorithm

Abstract

Conference

Keywords

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