Fuzzy rule extraction by bacterial memetic algorithms

In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg–Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of...

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Bibliographic Details
Main Author: Botzheim, J. (author)
Other Authors: Cabrita, Cristiano Lourenço (author), Kóczy, László T. (author), Ruano, Antonio (author)
Format: article
Language:eng
Published: 2013
Online Access:http://hdl.handle.net/10400.1/2237
Country:Portugal
Oai:oai:sapientia.ualg.pt:10400.1/2237
Description
Summary:In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg–Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of the evolutionary and the gradient-based learning techniques is usually called memetic algorithm. In this paper, a new kind of memetic algorithm, the bacterial memetic algorithm, is introduced for fuzzy rule extraction. The paper presents how the bacterial evolutionary algorithm can be improved with the Levenberg–Marquardt technique.