Bacterial memetic algorithm for fuzzy rule base optimization

Cristiano Cabrita; János Botzheim; Tamas D. Gedeon; António E. Ruano; László T. Kóczy; Carlos Fonseca

doi:10.1109/WAC.2006.376057

Bacterial memetic algorithm for fuzzy rule base optimization

Cristiano Cabrita^*, János Botzheim, Tamas D. Gedeon, António E. Ruano, László T. Kóczy, Carlos Fonseca

^*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

In our previous works model identification methods were discussed. The bacterial evolutionary algorithm for extracting a fuzzy rule base from a training set was presented. The LevenbergMarquardt method was also proposed for determining membership functions in fuzzy systems. The combination of evolutionary and gradient-based learning techniques - the bacterial memetic algorithm - was also introduced. In this paper an improvement of the bacterial memetic algorithm is shown for fuzzy rule extraction. The new method can optimize not only the rules, but can also find the optimal size of the rule base. Copyright - World Automation Congress (WAC) 2006.

Original language	English
Title of host publication	2006 World Automation Congress, WAC'06
Publisher	IEEE Computer Society
ISBN (Print)	1889335339, 9781889335339
DOIs	https://doi.org/10.1109/WAC.2006.376057
Publication status	Published - 2006
Event	2006 World Automation Congress, WAC'06 - Budapest, Hungary Duration: 24 Jun 2006 → 26 Jun 2006

Publication series

Name	2006 World Automation Congress, WAC'06

Conference

Conference	2006 World Automation Congress, WAC'06
Country/Territory	Hungary
City	Budapest
Period	24/06/06 → 26/06/06

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10.1109/WAC.2006.376057

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@inproceedings{6c5cab32e2164ffe897b42b17e3c36d8,

title = "Bacterial memetic algorithm for fuzzy rule base optimization",

abstract = "In our previous works model identification methods were discussed. The bacterial evolutionary algorithm for extracting a fuzzy rule base from a training set was presented. The LevenbergMarquardt method was also proposed for determining membership functions in fuzzy systems. The combination of evolutionary and gradient-based learning techniques - the bacterial memetic algorithm - was also introduced. In this paper an improvement of the bacterial memetic algorithm is shown for fuzzy rule extraction. The new method can optimize not only the rules, but can also find the optimal size of the rule base. Copyright - World Automation Congress (WAC) 2006.",

keywords = "Bacterial algorithm, Fuzzy rule base, Levenberg-marquardt method, Memetic algorithm",

author = "Cristiano Cabrita and J{\'a}nos Botzheim and Gedeon, {Tamas D.} and Ruano, {Ant{\'o}nio E.} and K{\'o}czy, {L{\'a}szl{\'o} T.} and Carlos Fonseca",

year = "2006",

doi = "10.1109/WAC.2006.376057",

language = "English",

isbn = "1889335339",

series = "2006 World Automation Congress, WAC'06",

publisher = "IEEE Computer Society",

booktitle = "2006 World Automation Congress, WAC'06",

address = "United States",

note = "2006 World Automation Congress, WAC'06 ; Conference date: 24-06-2006 Through 26-06-2006",

}

TY - GEN

T1 - Bacterial memetic algorithm for fuzzy rule base optimization

AU - Cabrita, Cristiano

AU - Botzheim, János

AU - Gedeon, Tamas D.

AU - Ruano, António E.

AU - Kóczy, László T.

AU - Fonseca, Carlos

PY - 2006

Y1 - 2006

N2 - In our previous works model identification methods were discussed. The bacterial evolutionary algorithm for extracting a fuzzy rule base from a training set was presented. The LevenbergMarquardt method was also proposed for determining membership functions in fuzzy systems. The combination of evolutionary and gradient-based learning techniques - the bacterial memetic algorithm - was also introduced. In this paper an improvement of the bacterial memetic algorithm is shown for fuzzy rule extraction. The new method can optimize not only the rules, but can also find the optimal size of the rule base. Copyright - World Automation Congress (WAC) 2006.

AB - In our previous works model identification methods were discussed. The bacterial evolutionary algorithm for extracting a fuzzy rule base from a training set was presented. The LevenbergMarquardt method was also proposed for determining membership functions in fuzzy systems. The combination of evolutionary and gradient-based learning techniques - the bacterial memetic algorithm - was also introduced. In this paper an improvement of the bacterial memetic algorithm is shown for fuzzy rule extraction. The new method can optimize not only the rules, but can also find the optimal size of the rule base. Copyright - World Automation Congress (WAC) 2006.

KW - Bacterial algorithm

KW - Fuzzy rule base

KW - Levenberg-marquardt method

KW - Memetic algorithm

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U2 - 10.1109/WAC.2006.376057

DO - 10.1109/WAC.2006.376057

M3 - Conference contribution

SN - 1889335339

SN - 9781889335339

T3 - 2006 World Automation Congress, WAC'06

BT - 2006 World Automation Congress, WAC'06

PB - IEEE Computer Society

T2 - 2006 World Automation Congress, WAC'06

Y2 - 24 June 2006 through 26 June 2006

ER -

Bacterial memetic algorithm for fuzzy rule base optimization

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