Design of Interval Type-2 Fuzzy Relation-Based Neuro-Fuzzy Networks for Nonlinear Process

  • Dong-Yoon Lee
  • Keon-Jun Park
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 339)


In this paper, we introduce the design of interval type-2 fuzzy relation-based neuro-fuzzy networks (IT2FRNFN) for modeling nonlinear process. IT2FRNFN is the network of combination between the neuro-fuzzy network (NFN) and interval type-2 fuzzy set with uncertainty. The premise part of the network is composed of the fuzzy relation division of input space and the consequence part of the network is represented by polynomial functions with interval set. And we also consider genetic algorithms to determine the structure and estimate the values of the parameters. The proposed network is evaluated with the nonlinear process.


Neuro-Fuzzy Networks (NFN) Interval Type-2 Fuzzy Set (IT2FS) Genetic Algorithms (GAs) Nonlinear process Modeling 


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  1. 1.
    Yamakawa, T.: A Neo Fuzzy Neuron and Its Application to System Identification and Prediction of the System Behavior. In: Proceeding of the 2nd International Conference on Fuzzy Logic & Neural Networks, pp. 447–483 (1992)Google Scholar
  2. 2.
    Buckley, J.J., Hayashi, Y.: Fuzzy neural networks: A survey. Fuzzy Sets Syst. 66, 1–13 (1994)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Information Science 8, 199–249 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Mizumoto, M., Tanaka, K.: Some Properties of Fuzzy Sets of Type-2. Information and Control 31, 312–340 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Karnik, N., Mendel, J., Liang, Q.: Type-2 Fuzzy Logic Systems. IEEE Trans. on Fuzzy Systems 7, 643–658 (1999)CrossRefGoogle Scholar
  6. 6.
    Liang, Q., Mendel, J.: Interval Type-2 Fuzzy Logic Systems: Theory and Design. IEEE Trans. on Fuzzy Systems 8, 535–550 (2000)CrossRefGoogle Scholar
  7. 7.
    Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, NJ (2001)zbMATHGoogle Scholar
  8. 8.
    Golderg, D.E.: Genetic Algorithm in search, Optimization & Machine Learning. Addison wesley (1989)Google Scholar
  9. 9.
    Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control, 2nd edn. Holden-Day, San Francisco (1976)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dong-Yoon Lee
    • 1
  • Keon-Jun Park
    • 2
  1. 1.Department of Electrical Electronic EngineeringJoongbu UniversityGeumsan-gunSouth Korea
  2. 2.Department of Electrical Information and Communication EngineeringWonkwang UniversityIksan-siSouth Korea

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