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International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing

RSFDGrC 2007: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing pp 119–126Cite as

Evolution of Fuzzy System Models: An Overview and New Directions

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Author information

Authors and Affiliations

  1. Dept. of Mechanical and Industrial Engineering, University of Toronto, Canada

    Aslı Çelikyılmaz & I. Burhan Türkşen

  2. Dept. of Industrial Engineering TOBB-Economics and Technology University, Turkey

    I. Burhan Türkşen

Authors
  1. Aslı Çelikyılmaz
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  2. I. Burhan Türkşen
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, York University, M3J 1P3, Toronto, Ontario, Canada

    Aijun An

  2. Institute of Computing Sciences, Poznań University of Technology, ul. Piotrowo 2, 60–965, Poznań, Poland

    Jerzy Stefanowski

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Cory J. Butz

  5. Department of Electrical and Computer Engineering, University of Alberta, T6G 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

  6. Institute of Compuer Science and Technology, Chongqing University of Posts and Telecommunications, 40065, Chongqing, P.R. China

    Guoyin Wang

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© 2007 Springer-Verlag Berlin Heidelberg

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Çelikyılmaz, A., Türkşen, I.B. (2007). Evolution of Fuzzy System Models: An Overview and New Directions. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_14

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  • DOI: https://doi.org/10.1007/978-3-540-72530-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72529-9

  • Online ISBN: 978-3-540-72530-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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