Fuzzy rule extraction by bacterial memetic algorithms
In our previous papers fuzzy model identification methods were discussed. The bacterial 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 evolution...
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| Outros Autores: | , , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2013
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.1/2306 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2306 |
| Resumo: | In our previous papers fuzzy model identification methods were discussed. The bacterial 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 will be called bacterial memetic algorithm. In this paper this new kind of memetic algorithm is introduced for fuzzy rule extraction. The paper presents how the bacterial algorithm can be improved with the Levenberg-Marquardt technique. |
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