Abstract
We will describe several methods to approximate the fuzzy rules tuning and generation problem. To generate the rules we will use several clustering algorithms. This approach supposes that the data lacks of structure. To tune the rules we will use two different techniques. One of them is based in descent gradients. The other one is based in a try to tune the rules outputs to reduce the error.
This work has been partially supported by CICYT project TIC97-1343-0002-02
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Delgado, M., Gómez-Skarmeta, A.F., Gómez Marín-Blázquez, J., Martinez Barberá, H. (1998). Fuzzy hybrid techniques in modeling. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_747
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DOI: https://doi.org/10.1007/3-540-64582-9_747
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