Advertisement

FSPN-Based Genetically Optimized Fuzzy Polynomial Neural Networks

  • Sung-Kwun Oh
  • Seok-Beom Roh
  • Daehee Park
  • Yong-Kab Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3483)

Abstract

In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy set-based polynomial neurons (FSPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

Keywords

Fuzzy Rule Triangular Membership Function Elitist Strategy Genetically Optimize Genetic Fuzzy System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cherkassky, V., Gehring, D., Mulier, F.: Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks 7, 969–984 (1996)CrossRefGoogle Scholar
  2. 2.
    Dickerson, J.A., Kosko, B.: Fuzzy function approximation with ellipsoidal rules. IEEE Trans. Syst., Man, Cybernetics. Part B 26, 542–560 (1996)CrossRefGoogle Scholar
  3. 3.
    Sommer, V., Tobias, P., Kohl, D., Sundgren, H., Lundstrom, L.: Neural networks and abductive networks for chemical sensor signals: A case comparison. Sensors and Actuators B 28, 217–222 (1995)CrossRefGoogle Scholar
  4. 4.
    Kleinsteuber, S., Sepehri, N.: A polynomial network modeling approach to a class of largescale hydraulic systems. Computers Elect. Eng. 22, 151–168 (1996)CrossRefGoogle Scholar
  5. 5.
    Cordon, O., et al.: Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems 141(1), 5–31 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Oh, S.K., Pedrycz, W.: Self-organizing Polynomial Neural Networks Based on Polynomial and Fuzzy Polynomial Neurons: Analysis and Design. Fuzzy Sets and Systems 142(2), 163–198 (2003)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1996)zbMATHGoogle Scholar
  8. 8.
    De Jong, K.A.: Are Genetic Algorithms Function Optimizers? In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, vol. 2. North-Holland, Amsterdam (1992)Google Scholar
  9. 9.
    Oh, S.K., Pedrycz, W.: Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks. Int. J. of General Systems 32, 237–250 (2003)zbMATHCrossRefGoogle Scholar
  10. 10.
    Wang, L.X., Mendel, J.M.: Generating fuzzy rules from numerical data with applications. IEEE Trans. Systems, Man, Cybern. 22, 1414–1427 (1992)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Crowder III., R.S.: Predicting the Mackey-Glass time series with cascade-correlation learning. In: Touretzky, D., Hinton, G., Sejnowski, T. (eds.) Proceedings of the 1990 Connectionist Models Summer School, Carnegic Mellon University, pp. 117–123 (1990)Google Scholar
  12. 12.
    Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. System, Man, and Cybern 23, 665–685 (1993)CrossRefGoogle Scholar
  13. 13.
    Maguire, L.P., Roche, B., McGinnity, T.M., McDaid, L.J.: Predicting a chaotic time series using a fuzzy neural network. Information Sciences 112, 125–136 (1998)zbMATHCrossRefGoogle Scholar
  14. 14.
    James, C.L., Huang, T.Y.: Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks. Applied Mathematical Modeling 23, 933–944 (1999)zbMATHCrossRefGoogle Scholar
  15. 15.
    Oh, S.K., Pedrycz, W., Ahn, T.C.: Self-organizing neural networks with fuzzy polynomial neurons. Applied Soft Computing 2, 1–10 (2002)CrossRefGoogle Scholar
  16. 16.
    Lapedes, A.S., Farber, R.: Non-linear Signal Processing Using Neural Networks: Prediction and System Modeling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (1987) Google Scholar
  17. 17.
    Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197, 287–289 (1977)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sung-Kwun Oh
    • 1
  • Seok-Beom Roh
    • 2
  • Daehee Park
    • 2
  • Yong-Kab Kim
    • 2
  1. 1.Department of Electrical EngineeringThe University of SuwonHwaseong-si, Gyeonggi-doSouth Korea
  2. 2.Department of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea

Personalised recommendations