Granular Evolving Min-Max Fuzzy Modeling

DOI

10.2991/eusflat-19.2019.3How to use a DOI?

Keywords

evolving systems fuzzy modeling fuzzy min-max algorithms

Abstract

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 adapted using expansion, 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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Alisson Porto
AU  - Fernando Gomide
PY  - 2019/08
DA  - 2019/08
TI  - Granular Evolving Min-Max Fuzzy Modeling
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 14
EP  - 21
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.3
DO  - 10.2991/eusflat-19.2019.3
ID  - Porto2019/08
ER  -