Abstract
In this paper we will present a novel approach to data-driven fuzzy modeling which aims to create highly accurate but also easily comprehensible models. This is achieved by a three-stage approach which separates the definition of the underlying fuzzy sets, the learning of the initial fuzzy model, and finally a local or global optimization of the resulting model. The benefit of this approach is that it allows to use a language comprising of comprehensible fuzzy predicates and to incorporate expert knowledge by defining problem specific fuzzy predicates. Furthermore, we achieve highly accurate results by applying a regularized optimization technique.
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Drobics, M., Himmelbauer, J. Creating comprehensible regression models. Soft Comput 11, 421–438 (2007). https://doi.org/10.1007/s00500-006-0107-1
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DOI: https://doi.org/10.1007/s00500-006-0107-1