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A New Approach to Self-Organizing Hybrid Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

We introduce a new category of fuzzy-neural networks-Hybrid Fuzzy Polynomial Neural Networks (HFPNN). These networks are based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and polynomial neurons (PNs). The augmented genetically optimized HFPNN (namely gHFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of HFPNN leads to the selection of preferred nodes (FPNs or PNs) available within the HFPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning.

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References

  1. Ivahnenko, A.G.: Polynomial theory of complex systems. IEEE Trans. on Systems, Man and Cybernetics SMC-12, 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. Oh, S.-K., Pedrycz, W., Kim, D.-W.: Hybrid Fuzzy Polynomial Neural Networks. Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems. 10, 257–280 (2002)

    Article  MATH  Google Scholar 

  4. Park, B.-J., Pedrycz, W., Oh, S.-K.: Fuzzy Polynomial Neural Networks: Hybrid Architectures of Fuzzy Modeling. IEEE Transaction on Fuzzy Systems 10, 607–621 (2002)

    Article  Google Scholar 

  5. Jong, D., A., K.: Are Genetic Algorithms Function Optimizers? In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature 2, North-Holland, Amsterdam

    Google Scholar 

  6. Wang, L.X., Mendel, J.M.: Generating fuzzy rules from numerical data with applications. IEEE Trans. Systems, Man, Cybern. 22, 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

  7. 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 

  8. Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Trans. System, Man, and Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  9. 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)

    Article  MATH  Google Scholar 

  10. James Li, C., 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)

    Article  MATH  Google Scholar 

  11. Oh, S.-K., Pedrycz, W., Ahn, T.-C.: Self-organizing neural networks with fuzzy polynomial neurons. Applied Soft Computing 2, 1–10 (2002)

    Article  Google Scholar 

  12. Lapedes, 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 

  13. Park, B.-J., Lee, D.-Y., Oh, S.-K.: Rule-Based Fuzzy Polynomial Neural Networks in Modeling Software Process Data. International Journal of Control, Automation and Systems 1(3), 321–331 (2003)

    Google Scholar 

  14. Park, H.-S., Oh, S.-K.: Rule-based Fuzzy-Neural Networks Using the Identification Algorithm of GA hybrid Scheme. International Journal of Control, Automation and Systems 1(1), 101–110 (2003)

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

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

    Ho-Sung Park, Sung-Kwun Oh, Daehee Park & Yongkab Kim

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2G6

    Witold Pedrycz

  3. Canada and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Ho-Sung Park
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  2. Sung-Kwun Oh
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  3. Witold Pedrycz
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  4. Daehee Park
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  5. Yongkab Kim
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Park, HS., Oh, SK., Pedrycz, W., Park, D., Kim, Y. (2004). A New Approach to Self-Organizing Hybrid Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_28

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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