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Fuzzy Integral for Rule Aggregation in Fuzzy Inference Systems

  • Leary Tomlin
  • Derek T. Anderson
  • Christian Wagner
  • Timothy C. Havens
  • James M. Keller
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 610)

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 non-additivity 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 non-convex, 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.

Keywords

Fuzzy inference system Choquet integral Fuzzy integral gFI NDFI Fuzzy measure 

Notes

Acknowledgments

This work has been partially supported by the U.S. Army Research Office, the U.S. Army and RDECOM CERDEC NVESD via W911NF-16-1-0017, W911NF-14-1-0114, W911NF-14-1-0673, W909MY-13-C-0013 and 57940-EV. This work was also partially funded by RCUK’s EP/M02315X/1.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Leary Tomlin
    • 1
  • Derek T. Anderson
    • 1
  • Christian Wagner
    • 2
  • Timothy C. Havens
    • 3
  • James M. Keller
    • 4
  1. 1.Electrical and Computer EngineeringMississippi State UniversityStarkvilleUSA
  2. 2.School of Computer ScienceUniversity of NottinghamNottinghamUK
  3. 3.Electrical and Computer Engineering, Computer ScienceMichigan Technological UniversityHoughtonUSA
  4. 4.Electrical and Computer Engineering, Computer ScienceUniversity of MissouriColumbiaUSA

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