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Erschienen in:
2003 | OriginalPaper | Buchkapitel
Conciseness of Fuzzy Models
verfasst von : Toshihiro Suzuki, Takeshi Furuhashi
Erschienen in: Interpretability Issues in Fuzzy Modeling
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Fuzzy models are used to describe input-output relationships of unknown nonlinear systems in an interpretable manner for humans. Interpretability is one of the indispensable features of fuzzy models, which is closely related to their conciseness. The authors introduce the conciseness of fuzzy models, based on observations that humans grasp the input-output relationships by granules. The conciseness measure is then formulated by introducing De Luca and Termini’s fuzzy entropy and a new measure is derived from the analogy of relative entropy. This chapter also discusses the conflicting relationships between the conciseness and the accuracy of fuzzy models. A fuzzy modeling with Pareto optimal solutions is presented. Numerical experiments are done to demonstrate the effects of the conciseness measure.