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Evolution of Fuzzy System Models: An Overview and New Directions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4482)

Abstract

Fuzzy System Models (FSM), as one of the constituents of soft computing methods, are used for mining implicit or unknown knowledge by approximating systems using fuzzy set theory. The undeniable merit of FSM is its inherent ability of dealing with uncertain, imprecise, and incomplete data and still being able to make powerful inferences. This paper provides an overview of FSM techniques with an emphasis on new approaches on improving the prediction performances of system models. A short introduction to soft computing methods is provided and new improvements in FSMs, namely, Improved Fuzzy Functions (IFF) approaches is reviewed. IFF techniques are an alternate representation and reasoning schema to Fuzzy Rule Base (FRB) approaches. Advantages of the new improvements are discussed.

Keywords

Fuzzy systems soft computing data mining knowledge discovery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Dept. of Mechanical and Industrial Engineering, University of TorontoCanada
  2. 2.Dept. of Industrial Engineering TOBB-Economics and Technology UniversityTurkey

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