Abstract
The membership functions of fuzzy expert system need a systematic, self-learning method instead of subjectively tuning method in order to increase the performance of the fuzzy model. The genetic-algorithm learning method is consequently employed. The rule-based encoding scheme would bring the redundant information for the genetic algorithm by repeatedly representing the similar membership function in an individual. The new encoding method, which is parameter-based encoding scheme, would diminish the redundant representation of fuzzy parameters. This method would separate the data structures of fuzzy rules and fuzzy parameters in the genetic-algorithm learning method. This method should efficiently use the memory resources of computers and increase the dimensions of the solved problem. Then, a numerical example and the learning results are demonstrated. Discussions about the effects of population size, reproduction method, crossover rate, mutation rate and fitness scaling are included. Finally, some conclusion remarks.
| Original language | English |
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| Pages | 406-411 |
| Number of pages | 6 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 Asian Fuzzy Systems Symposium - Kenting, Taiwan Duration: 11 Dec 1996 → 14 Dec 1996 |
Conference
| Conference | Proceedings of the 1996 Asian Fuzzy Systems Symposium |
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| City | Kenting, Taiwan |
| Period | 11/12/96 → 14/12/96 |