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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2553 |
| Date | January 2007 |
| Creators | Aimejalii, K., Dahal, Keshav P., Hossain, M. Alamgir |
| Publisher | IEEE |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Conference paper, Accepted manuscript |
| Rights | 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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