A rapid-prototyping approach to fuzzy system modeling

Ya-chen Hsu; Guanrong Chen

doi:10.1109/NAFIPS.1998.715520

A rapid-prototyping approach to fuzzy system modeling

Ya-chen Hsu
, Guanrong Chen

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

We develop a three-phase design scheme for fuzzy modeling using input-output training data. The first step is to establish an approximate rule base using a clustering method, then the selective rule activation technique is applied to resize the rule base, and finally the parameters are fine-tuned by the backpropagation algorithm. Simulation results show that this scheme generates a good fuzzy model that can successfully predict the system output and the rules selected by the selective rule activation technique are appropriate for generalizing different sets of data.

Original language	English
Title of host publication	1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS
Editors	Jim Bezdek, Lawrence 0. Hall
Publisher	IEEE
Pages	15-19
ISBN (Print)	0-7803-4453-7 , 0-7803-4454-5
DOIs	https://doi.org/10.1109/NAFIPS.1998.715520
Publication status	Published - Aug 1998
Externally published	Yes
Event	1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998 - Pensacola Beach, United States Duration: 20 Aug 1998 → 21 Aug 1998

Publication series

Name	Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Conference

Conference	1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998
Place	United States
City	Pensacola Beach
Period	20/08/98 → 21/08/98

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/NAFIPS.1998.715520

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title = "A rapid-prototyping approach to fuzzy system modeling",

abstract = "We develop a three-phase design scheme for fuzzy modeling using input-output training data. The first step is to establish an approximate rule base using a clustering method, then the selective rule activation technique is applied to resize the rule base, and finally the parameters are fine-tuned by the backpropagation algorithm. Simulation results show that this scheme generates a good fuzzy model that can successfully predict the system output and the rules selected by the selective rule activation technique are appropriate for generalizing different sets of data.",

author = "Ya-chen Hsu and Guanrong Chen",

year = "1998",

month = aug,

doi = "10.1109/NAFIPS.1998.715520",

language = "English",

isbn = "0-7803-4453-7 ",

series = "Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS",

publisher = "IEEE",

pages = "15--19",

editor = "Bezdek, \{Jim \} and \{Hall \}, \{Lawrence 0. \}",

booktitle = "1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS",

address = "United States",

note = "1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998 ; Conference date: 20-08-1998 Through 21-08-1998",

}

Hsu, Y & Chen, G 1998, A rapid-prototyping approach to fuzzy system modeling. in J Bezdek & L Hall (eds), 1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, IEEE, pp. 15-19, 1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998, Pensacola Beach, United States, 20/08/98. https://doi.org/10.1109/NAFIPS.1998.715520

A rapid-prototyping approach to fuzzy system modeling. / Hsu, Ya-chen; Chen, Guanrong.
1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS. ed. / Jim Bezdek; Lawrence 0. Hall . IEEE, 1998. p. 15-19 (Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A rapid-prototyping approach to fuzzy system modeling

AU - Hsu, Ya-chen

AU - Chen, Guanrong

PY - 1998/8

Y1 - 1998/8

N2 - We develop a three-phase design scheme for fuzzy modeling using input-output training data. The first step is to establish an approximate rule base using a clustering method, then the selective rule activation technique is applied to resize the rule base, and finally the parameters are fine-tuned by the backpropagation algorithm. Simulation results show that this scheme generates a good fuzzy model that can successfully predict the system output and the rules selected by the selective rule activation technique are appropriate for generalizing different sets of data.

AB - We develop a three-phase design scheme for fuzzy modeling using input-output training data. The first step is to establish an approximate rule base using a clustering method, then the selective rule activation technique is applied to resize the rule base, and finally the parameters are fine-tuned by the backpropagation algorithm. Simulation results show that this scheme generates a good fuzzy model that can successfully predict the system output and the rules selected by the selective rule activation technique are appropriate for generalizing different sets of data.

UR - https://www.scopus.com/pages/publications/84960471616

UR - https://www.scopus.com/pages/publications/84960471616#tab=impact

U2 - 10.1109/NAFIPS.1998.715520

DO - 10.1109/NAFIPS.1998.715520

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 0-7803-4453-7

SN - 0-7803-4454-5

T3 - Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS

SP - 15

EP - 19

BT - 1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS

A2 - Bezdek, Jim

A2 - Hall , Lawrence 0.

PB - IEEE

T2 - 1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998

Y2 - 20 August 1998 through 21 August 1998

ER -

A rapid-prototyping approach to fuzzy system modeling

Abstract

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