Abstract
The fuzzy Takagi-Sugeno-Kang model and the inference system proposed by these authors is a very powerful tool for function approximation problems due to its capability of expressing a complex nonlinear system using a set of simple linear rules. Nevertheless, during the learning and optimization process, usually a trade-off has to be carried out among global system accuracy and sub-models (rules) interpretability. In this paper we review the TaSe model [8] for function approximation (for Grid-Based Fuzzy Systems and extend it to consider Clustering-Based Fuzzy Systems) that is learned from an I/O numerical data set and that will allow us to extract strong interpretable rules, whose consequents are the Taylor Series Expansion of the model output around the rule centres. This TaSe model provides full interpretability to the local models with high accuracy in the global approximation. The rule extraction process using the TaSe model and its properties will be reviewed using a significant example.
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Herrera, L.J., Pomares, H., Rojas, I., Guilén, A., Awad, M., González, J. (2005). Interpretable Rule Extraction and Function Approximation from Numerical Input/Output Data Using the Modified Fuzzy TSK Model, TaSe Model. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_42
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DOI: https://doi.org/10.1007/11548669_42
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