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Design Methodology of Optimized IG_gHSOFPNN and Its Application to pH Neutralization Process

  • Ho-Sung Park
  • Kyung-Won Jang
  • Sung-Kwun Oh
  • Tae-Chon Ahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

In this paper, we propose design methodology of optimized Information granulation based genetically optimized Hybrid Self-Organizing Fuzzy Polynomial Neural Networks (IG_gHSOFPNN) by evolutionary optimization. The augmented IG_gHSOFPNN results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HSOFPNN. The GA-based design procedure being applied at each layer of IG_gHSOFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HSOFPNN. The obtained results demonstrate superiority of the proposed networks over the existing fuzzy and neural models.

Keywords

Performance Index Fuzzy Rule Information Granulation Genetic Optimization Polynomial Neural Network 
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.
    Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. on Systems, Man and Cybernetics SMC-1, 364–378 (1971)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Oh, S.K., Pedrycz, W.: The design of self-organizing Polynomial Neural Networks. Information Science 141, 237–258 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Park, H.S., Park, K.J., Lee, D.Y., Oh, S.K.: Advanced Self-Organizing Neural Networks Based on Competitive Fuzzy Polynomial Neurons. Transactions of The Korean Institute of Electrical Engineers 53D, 135–144 (2004)Google Scholar
  4. 4.
    Oh, S.K., Pedrycz, W., Park, H.S.: Multi-layer hybrid fuzzy polynomial neural networks: a design in the framework of computational intelligence. Neurocomputing 64, 397–431 (2005)CrossRefGoogle Scholar
  5. 5.
    Jong, D.K.A.: Are Genetic Algorithms Function Optimizers? In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, North-Holland, Amsterdam (1992)Google Scholar
  6. 6.
    Zadeh, L.A., et al.: Fuzzy Sets and Applications: Selected Paper. Wiley, New York (1987)Google Scholar
  7. 7.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)MATHGoogle Scholar
  8. 8.
    Hall, R.C., Seberg, D.E.: Modeling and Self-Tuning Control of a Multivariable pH Neutrallization Process. In: Proc. ACC, pp. 1822–1827 (1989)Google Scholar
  9. 9.
    McAvoy, T.J.: Time optimal and Ziegler-Nichols control. Ind. Eng. Chem. Process Des. Develop. 11, 71–78 (1972)CrossRefGoogle Scholar
  10. 10.
    Pajunen, G.A.: Comparison of linear and nonlinear adaptive control of a pH-process. IEEE Control Systems Maganize, 39–44 (1987)Google Scholar
  11. 11.
    Nie, J., Loh, A.P., Hang, C.C.: Modeling pH neutralization processes using fuzzy-neurla approaches. Fuzzy Sets and Systems 78, 5–22 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ho-Sung Park
    • 1
  • Kyung-Won Jang
    • 1
  • Sung-Kwun Oh
    • 2
  • Tae-Chon Ahn
    • 1
  1. 1.School of Electrical Electronic and Information EngineeringWonkwang UniversityChon-BukSouth Korea
  2. 2.Department of Electrical EngineeringThe University of SuwonGyeonggi-doSouth Korea

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