Abstract
This paper discusses the concepts of linguistic integrity and interpretability. The concepts are used as a framework to design an algorithm to construct linguistic fuzzy models from Numerical Data. The constructed model combines prior knowledge (if present) and numerical information. Two algorithms are presented in this chapter. The main algorithm is the Autonomous Fuzzy Rule Extractor with Linguistic Integrity (AFRELI). This algorithm is complemented with the use of the Fu Zion algorithm created to merge consecutive membership functions while guar anteeing the distinguishability between fuzzy sets. Examples of function approximations and modeling of industrial data are presented as application examples.
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Espinosa, J., Vandewalle, J. (2003). Extracting Linguistic Fuzzy Models from Numerical Data-AFRELI Algorithm. 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_5
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DOI: https://doi.org/10.1007/978-3-540-37057-4_5
Publisher Name: Springer, Berlin, Heidelberg
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