Abstract
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.
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Suzuki, T., Furuhashi, T. (2003). Conciseness of Fuzzy Models. 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_24
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DOI: https://doi.org/10.1007/978-3-540-37057-4_24
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