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Fuzzy Granulation-Based Cascade Fuzzy Neural Networks Optimized by GA-RSL

  • Chang-Wook Han
  • Jung-Il Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)

Abstract

This paper is concerned with cascade fuzzy neural networks and its optimization. These networks come with sound and transparent logic characteristics by being developed with the aid of AND and OR fuzzy neurons and subsequently logic processors (LPs). We discuss main functional properties of the model and relate them to its form of cascade type of systems formed as a stack of LPs. The structure of the network that deals with a selection of a subset of input variables and their distribution across the individual LPs is optimized with the use of genetic algorithms (GA). We discuss random signal-based learning (RSL), a local search technique, aimed at further refinement of the connections of the neurons (GA-RSL). We elaborate on the interpretation aspects of the network and show how this leads to a Boolean or multi-valued logic description of the experimental data. Two kinds of standard data sets are discussed with respect to the performance of the constructed networks and their interpretability.

Keywords

Genetic Algorithm Fuzzy Neural Network Local Search Technique Genetic Algorithm Mode Logic Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pedrycz, W., Reformat, M., Han, C.W.: Cascade Architectures of Fuzzy Neural Networks. Fuzzy Optimization and Decision Making 3(1), 5–37 (2004)CrossRefMATHGoogle Scholar
  2. 2.
    Babuska, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers, Norwell (1998)CrossRefGoogle Scholar
  3. 3.
    Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2002)MATHGoogle Scholar
  4. 4.
    Mitaim, S., Kosko, B.: The Shape of Fuzzy Sets in Adaptive Function Approximation. IEEE Trans. Fuzzy Systems 9(4), 647–656 (2001)CrossRefGoogle Scholar
  5. 5.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)MATHGoogle Scholar
  6. 6.
    Michalewicz, Z.: Genetic Algorithm + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin (1996)CrossRefMATHGoogle Scholar
  7. 7.
    Han, C.W., Park, J.I.: Design of a Fuzzy Controller using Random Signal-based Learning Employing Simulated Annealing. In: Proc. of the 39th IEEE Conference on Decision and Control, pp. 396–397 (2000)Google Scholar
  8. 8.
    Han, C.W., Park, J.I.: A Study on Hybrid Genetic Algorithms using Random Signal-based Learning Employing Simulated Annealing. In: Proc. of the 2001 American Control Conference, pp. 198–199 (2001)Google Scholar
  9. 9.
    Han, C.W., Park, J.I.: A Study on Hybrid Random Signal-based Learning ant Its Applications. International Journal of Systems Science 35(4), 243–253 (2004)CrossRefMATHGoogle Scholar
  10. 10.
    Kilic, K., Sproule, B.A., Turksen, I.B., Naranjo, C.A.: A Fuzzy System Modeling Algorithm for Data Analysis and Approximate Reasoning. Robotics and Autonomous Systems 49, 173–180 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chang-Wook Han
    • 1
  • Jung-Il Park
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceYeungnam UniversityGyongbukSouth Korea

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