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Extraction of fuzzy rules using sensibility analysis in a neural network

J. Besada, M.A. Sanz-Bobi

International Conference on Artificial Neural Networks - ICANN'02, Madrid (España). 27 agosto 2002


Resumen:
This paper proposes a new method for the extraction of knowledge from a trained type feed-forward neural network. The new knowledge extracted is expressed by fuzzy rules directly from a sensibility analysis between the inputs and outputs of the relationship that model the neural network. This easy method of extraction is based on the similarity of a fuzzy set with the derivative of the tangent hyperbolic function used as an activation function in the hidden layer of the neural network. The analysis performed is very useful, not only for the extraction of knowledge, but also to know the importance of every rule extracted in the whole knowledge and, furthermore, the importance of every input stimulating the neural network.


Palabras clave: Neuro-fuzzy models, rule extraction, sensibility analysis, knowledge discovering


Fecha de publicación: agosto 2002.



Cita:
J. Besada, M.A. Sanz-Bobi, Extraction of fuzzy rules using sensibility analysis in a neural network, International Conference on Artificial Neural Networks - ICANN'02, Madrid (España). 27-31 Agosto 2002.


    Líneas de investigación:
  • *Predicción y Análisis de Datos
  • *Modelado, Simulación y Optimización

IIT-02-003A

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