Abstract
A great deal of research has been carried out into the main architectures, learning abilities and applications of fuzzy systems. Studies have addressed the problem of selecting different T-norm, T-conorm, types of membership function, different defuzzifier operator and fuzzy implication operator; these constitute the essential functional components of fuzzy inference process. In this paper, and statistical analyses have been carried out into the influence on the behaviour of the fuzzy system arising from the use of different alternatives of the main functional block. Thus, as a complement to the existing intuitive knowledge, it is necessary to have a more precise understanding of the significance of the different alternatives. In the present contribution, the relevance and relative importance of the parameters involved in such a design are investigated by using a statistical tool, the ANalysis Of the VAriance (ANOVA).
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Valenzuela, O., Marquez, L., Pasadas, M., Rojas, I., Rodriguez, M., Rojas, F. (2004). Analysis of the Functional Block and Operator Involved in Fuzzy System Design. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_16
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DOI: https://doi.org/10.1007/978-3-540-25945-9_16
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