Abstract
This chapter addresses rule base complexity in fuzzy models obtained from data. Data-driven fuzzy modeling is introduced, and two main approaches to complexity reduction in fuzzy rule-based models are presented: Similarity-driven rule base simplification, and rule reduction with orthogonal transforms.
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Setnes, M. (2003). Simplification and reduction of fuzzy rules. In: Casillas, J., Cordón, O., Herrera, F., Magdalena, L. (eds) Interpretability Issues in Fuzzy Modeling. Studies in Fuzziness and Soft Computing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37057-4_12
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DOI: https://doi.org/10.1007/978-3-540-37057-4_12
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