Skip to main content
SpringerLink
Log in

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

  • Conference paper
Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

Included in the following conference series:

  • 2196 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. on Systems, Man and Cybernetics SMC-1, 364–378 (1971)

    Article  MathSciNet  Google Scholar 

  2. Oh, S.K., Pedrycz, W.: The design of self-organizing Polynomial Neural Networks. Information Science 141, 237–258 (2002)

    Article  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Zadeh, L.A., et al.: Fuzzy Sets and Applications: Selected Paper. Wiley, New York (1987)

    Google Scholar 

  7. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)

    MATH  Google Scholar 

  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. McAvoy, T.J.: Time optimal and Ziegler-Nichols control. Ind. Eng. Chem. Process Des. Develop. 11, 71–78 (1972)

    Article  Google Scholar 

  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. Nie, J., Loh, A.P., Hang, C.C.: Modeling pH neutralization processes using fuzzy-neurla approaches. Fuzzy Sets and Systems 78, 5–22 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Electronic and Information Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Chon-Buk, 570-749, South Korea

    Ho-Sung Park, Kyung-Won Jang & Tae-Chon Ahn

  2. Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea

    Sung-Kwun Oh

Authors
  1. Ho-Sung Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyung-Won Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sung-Kwun Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tae-Chon Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Park, HS., Jang, KW., Oh, SK., Ahn, TC. (2006). Design Methodology of Optimized IG_gHSOFPNN and Its Application to pH Neutralization Process. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_119

Download citation

  • DOI: https://doi.org/10.1007/11893295_119

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation