Fuzzy integral for rule aggregation in fuzzy inference systems
Document Type
Conference Proceeding
Publication Date
6-11-2016
Department
Department of Electrical and Computer Engineering
Abstract
The fuzzy inference system (FIS) has been tuned and revamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving one key component relatively underrepresented, rule aggregation. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires nonadditivity and exploitation of interactions between rules. Herein, we show how the fuzzy integral, a parametric non-linear aggregation operator, can be used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to “unrestricted” fuzzy sets, i.e., subnormal and nonconvex, makes this now possible. We explore the role of two extensions, the gFI and the NDFI, discuss when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.
Publication Title
Communications in Computer and Information Science
ISBN
978-3-319-40595-7
Recommended Citation
Tomlin, L.,
Anderson, D.,
Wagner, C.,
Havens, T. C.,
&
Keller, J.
(2016).
Fuzzy integral for rule aggregation in fuzzy inference systems.
Communications in Computer and Information Science,
610, 78-90.
http://doi.org/10.1007/978-3-319-40596-4_8
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/4104