Referenced Items are not available for this document.
The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown in this paper that the performance of fuzzy control systems may be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller
Published in:
Systems, Man and Cybernetics, IEEE Transactions on
(Volume:24
,
Issue:
1
)
Date of Publication: Jan 1994
- Page(s):
- 39 - 47
- ISSN :
- 0018-9472
- INSPEC Accession Number:
- 4644899
- Digital Object Identifier :
- 10.1109/21.259684
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Jan 1994
- Sponsored by :
- IEEE Systems, Man, and Cybernetics Society
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