A Linear-Complexity Rule Base Generation Method for Fuzzy Systems
- https://doi.org/10.2991/ifsa-eusflat-15.2015.75How to use a DOI?
- Inductive rule learning, fuzzy rulebased model, double-consequent linguistic rules, complexity-accuracy trade-off, predictability.
Rule base generation from numerical data has been a dynamic research topic within the fuzzy community in the last decades, and several well-established methods have been proposed. While some authors presented simple, empirical approaches, but which generally show high error rates, others turned to complex heuristic techniques to improve accuracy. In this paper, an extension of the classical Wang-Mendel method is proposed. While keeping a linear complexity, the new method achieves performances close to those of more complex methods based on cooperative rules (COR). Results on synthetic data show the potential of the proposed method as a complexity-accuracy trade-off.
- © 2015, 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/).
Cite this article
TY - CONF AU - Liviu-Cristian Dutu AU - Gilles Mauris AU - Philippe Bolon PY - 2015/06 DA - 2015/06 TI - A Linear-Complexity Rule Base Generation Method for Fuzzy Systems BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 520 EP - 527 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.75 DO - https://doi.org/10.2991/ifsa-eusflat-15.2015.75 ID - Dutu2015/06 ER -