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Bacterial memetic algorithm for fuzzy rule base optimization

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Date

2006

Authors

Cabrita, Cristiano
Botzheim, Janos
Gedeon, Tamas (Tom)
Ruano, Antonio E
Fonseca, Carlos
Koczy, Lazlo

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

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.

Description

Keywords

Keywords: Bacteria; Functions; Fuzzy rules; Learning algorithms; Logic programming; Optimization; Bacterial algorithms; Fuzzy rule base; Levenberg-marquardt methods; Memetic algorithms; Evolutionary algorithms Bacterial algorithm; Fuzzy rule base; Levenberg-marquardt method; Memetic algorithm

Citation

URI

http://hdl.handle.net/1885/36583

Collections

ANU Research Publications

Source

Proceedings of World Automation Conference 2006

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1109/WAC.2006.376057

Restricted until

2037-12-31

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01_Cabrita_Bacterial_memetic_algorithm_2006.pdf (597.97 KB)

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