An Advanced Design Methodology of Fuzzy Set-Based Polynomial Neural Networks with the Aid of Symbolic Gene Type Genetic Algorithms and Information Granulation

  • Seok-Beom Roh
  • Hyung-Soo Hwang
  • Tae-Chon Ahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


In this paper, we propose a new design methodology that adopts Information Granulation to the structure of fuzzy-neural networks called Fuzzy Set-based Polynomial Neural Networks (FSPNN). We find the optimal structure of the proposed model with the aid of symbolic genetic algorithms which has symbolic gene type chromosomes. We are able to find information related to real system with Information Granulation through numerical data. Information Granules obtained from Information Granulation help us understand real system without the field expert. In Information Granulation, we use conventional Hard C-Means Clustering algorithm and proposed procedure that handle the apex of clusters using ‘Union’ and ‘Intersection’ operation. We use genetic algorithm to find optimal structure of the proposed networks. The proposed networks are based on GMDH algorithm that makes whole networks dynamically. In other words, FSPNN is built dynamically with symbolic genetic algorithms. Symbolic gene type has better characteristic than binary coding GAs from the size of solution space’s point of view. Symbolic genetic algorithms are capable of reducing the solution space more than conventional genetic algorithms with binary genetype chromosomes. The performance of genetically optimized FSPNN (gFSPNN) with aid of symbolic genetic algorithms is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling.


Genetic Algorithm Fuzzy Rule Information Granulation Consequent Part Fuzzy Identification 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Oh, S.K., Pedrycz, W.: The design of self-organizing Polynomial Neural Networks. Information Science 141, 237–258 (2002)CrossRefMATHGoogle Scholar
  2. 2.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)MATHGoogle Scholar
  3. 3.
    Goldberg, D.E.: Genetic algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  4. 4.
    Oh, S.-K., Pedrycz, W.: Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks. Int. J. of General Systems. 32(3), 237–250 (2003)CrossRefMATHGoogle Scholar
  5. 5.
    Wang, L.X., Mendel, J.M.: Generating fuzzy rules from numerical data with applications. IEEE Trans 22(6), 1414–1427 (1992)MathSciNetGoogle Scholar
  6. 6.
    Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. System, Man, and Cybern. 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Oh, S.-K., Pedrycz, W., Ahn, T.-C.: Self-organizing neural networks with fuzzy polynomial neurons. Applied Soft Computing 2(1F), 1–10 (2002)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefMATHGoogle Scholar
  9. 9.
    Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets System 90, 111–117 (1997)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seok-Beom Roh
    • 1
  • Hyung-Soo Hwang
    • 1
  • Tae-Chon Ahn
    • 1
  1. 1.Department of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea

Personalised recommendations