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GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks

Aimejalii, K.
; 
Dahal, Keshav P.
; 
Hossain, M. Alamgir
Publication Date
2007
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Keywords
Fuzzy neural networks, Fuzzy rules, Genetic algorithms
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Copyright © [2007] IEEE. Reprinted from Seventh International Conference on Intelligent Systems Design and Applications, ISDA 2007. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubspermissions@ ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it
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Abstract
Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform ruleselection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection.
URI
http://hdl.handle.net/10454/2553
Version
Accepted manuscript
Citation
Aimejalii K, Dahal K and Hossain A (2007) GA-based learning algorithms to identify fuzzy rules for fuzzy neural networks. In: Seventh International Conference on Intelligent Systems Design and Applications, ISDA 2007, Rio de Janeiro, 20-24th Oct., 2007. New York: IEEE.
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https://doi.org/10.1109/ISDA.2007.10
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Conference paper
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