Evolving Granular Fuzzy Min-Max Modeling

Porto, Alisson; Gomide, Fernando

doi:10.1007/978-3-319-95312-0_4

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“…input data space using hyperboxes and associates a functional fuzzy rule to each hyperbox. The input space granulation is an enhanced version of the one reported in [8]. A hyperbox reduction process is developed to improve rule robustness in the presence of concept drift, and to increase prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Granular Evolving Min-Max Fuzzy Modeling

Porto¹,

Gomide²

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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The paper addresses a novel evolving functional fuzzy modeling algorithm using hyperboxes and min-max fuzzy granulation. Data space granulation is done as data are input, and undergoes never ending adaptation using expansion and reduction operations to encompass new information. A fuzzy rule is assigned to each hyperbox using Gaussian membership functions in the rule antecedents, and affine functions in the rule consequents. The algorithm is fast, simple, and interpretable. Computational evaluation using time series modeling and nonlinear system identification experiments shows that the granular evolving min-max fuzzy modeling algorithm outperforms current state of the art evolving algorithms counterparts.

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Section: Introductionmentioning

confidence: 99%