The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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Self-Organizing Fuzzy Polynomial Neural Networks by Means of IG-based Consecutive Optimization : Design and Analysis

정보 입자기반 연속전인 최적화를 통한 자기구성 퍼지 다항식 뉴럴네트워크 : 설계와 해석

  • 박호성 (원광대학 제어계측공학과) ;
  • 오성권 (수원대학 전기전자공학부)
  • Published : 2006.06.01
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Abstract

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

Keywords

References

  1. W. Pedrycz, J. F. Peters, Computational Intelligence and Software Engineering, World Scientific, Singapore, 1998
  2. W. Pedrycz, Fuzzy system development: software methodology and design tools, Int. Handbook of Fuzzy Sets and Possibility Theory, Kluwer Academic Publishers, in press
  3. A. G. Ivakhnenko, 'Polynomial theory of complex systems', IEEE Trans. on Systems, Man and Cybernetics, Vol. SMC-1, pp. 364-278, 1971
  4. S. K. Oh and W. Pedrycz, 'The design of self-organizing Polynomial Neural Networks', Information Science, Vol. 141, pp. 237-258, 2002 https://doi.org/10.1016/S0020-0255(02)00175-5
  5. 박호성, 오성권, 윤양웅, '퍼지 뉴럴 네트워크 구조로의 새로운 모델링 연구', 제어·자동화·시스템 공학회, Vol. 7, No.8, pp. 664-673, 2001. 8
  6. Holland, J. H., Adaptation In Natural and Artificial Systems, The University of Michigan Press, Ann Arbour. 1975
  7. K. De Jong. Are genetic algorithms function optimizers? In Proc. of PPSN II (Parallel Problem Solving from Nature), pages 3-13, Amsterdam, North Holland, 1992
  8. R. Moore, Interval analysis, Prentice-Hall, Englewood Cliffs, NJ, 1966.
  9. R. Moore (Ed.), Reliability in Computing, Academic Press, New York, 1988
  10. L. A. Zadeh, in: L.A. Zadeh, R. Yager, et al., (Eds.), Fuzzy Sets and Applications: Selected Papers, Wiley, New York, 1987
  11. J. C. BEZDEK, Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum, 1981
  12. D. E. Box and G. M. Jenkins, Time Series Analysis, Forcasting and Control, California : Holden Day, 1976
  13. W. Pedrycz, 'An identification algorithm in fuzzy relational system', Fuzzy Sets Syst., Vol. 13, pp, 153-167, 1984 https://doi.org/10.1016/0165-0114(84)90015-0
  14. J. Leski and E. Czogala, 'A new artificial neural networks based fuzzy inference system with moving consequents in if-then rules and selected applications', Fuzzy Sets and Systems, Vol. 108, pp. 289-297, 1999 https://doi.org/10.1016/S0165-0114(97)00314-X
  15. W. Pedrycz, G. Vukovich, 'Granular neural networks', Neurocomputing, Vol. 36, pp. 205-224. 2001 https://doi.org/10.1016/S0925-2312(00)00342-8
  16. M. Sugeno and T. Yasukawa, 'A Fuzzy-Logie-Based Approach to Qualitative Modeling', IEEE Trans. Fuzzy Systems, Vol. 1, No.1, pp. 7-31, 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  17. J. Q. Chen, Y. G. Xi, and Z,J. Zhang, 'A clustering algorithm for fuzzy model identification', Fuzzy Sets and Systems, Vol. 98, pp, 319-329, 1998 https://doi.org/10.1016/S0165-0114(96)00384-3
  18. A. F. Gomez-Skarmeta, M. Delgado and M.A. Vila, 'About the use of fuzzy clustering techniques for fuzzy model identification', Fuzzy Sets and Systems, Vol. 106, pp. 179-188, 1999 https://doi.org/10.1016/S0165-0114(97)00276-5
  19. S. K. Oh and W. Pedrycz, 'Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems', Fuzzy sets and Systems, Vol. 115, No. 2, pp. 205-230, 2000 https://doi.org/10.1016/S0165-0114(98)00174-2
  20. E. T. Kim, et al, 'A simple identified Sugeno-type fuzzy model via double clustering', Information Science, Vol. 110, pp. 25-39, 1998 https://doi.org/10.1016/S0020-0255(97)10083-4
  21. Y. Lin, G. A. Cunningham III, 'A new approach to fuzzy-neural modeling', IEEE Trans. Fuzzy Systems, Vol.3, No. 2, pp. 190-197, 1995 https://doi.org/10.1109/91.388173
  22. S. K. Oh, W. Pedrycz, and D. W. Kim, 'Hybrid Fuzzy Polynomial Neural Networks', Int. J. of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 10, No. 3, pp. 257-280, June, 2002 https://doi.org/10.1142/S0218488502001478
  23. A. F. Gomez-Skarmeta, M. Delgado and M.A. Vila, 'About the use of fuzzy clustering techniques for fuzzy model identification', Fuzzy Sets and Systems, Vol. 106, pp. 179-188, 1999 https://doi.org/10.1016/S0165-0114(97)00276-5
  24. S. K. Oh, D. W. Kim, B. J. Park, and H. S. Hwang, 'Advanced Polynomial Neural Networks Architecture with New Adaptive Nodes', Transactions on Control, Automation and Systems Engineering, Vol. 3, No.1, pp. 43-50, March, 2001
  25. D. W. Kim, S. K. Oh, and H. K. Kim, 'A Study on the Self-organizing Fuzzy Polynomial Neural Networks', J. of Korean Institute of Electrical Engineers, Vol. 11, No.2, pp. 79-89, Jun., 2001
  26. G. Vachtsevanos, V. Ramani and T. W. Hwang, 'Prediction of Gas Turbine NOx Emissions using Polynomial Neural Network', Technical Report, Georgia Institute of Technology, Atlanta, 1995
  27. S. K. Oh, W. Pedrycz and H. S. Park, 'Hybrid Identification in Fuzzy-Neural Networks', Fuzzy Sets and Systems, Vol. 138, Issue 2, pp. 399-426, 2003 https://doi.org/10.1016/S0165-0114(02)00441-4
  28. H. S. Park and S. K. Oh, 'Fuzzy Relation-based Fuzzy Neural-Networks Using a Hybrid Identification Algorithm', International Journal of Control, Automation, and Systems, Vol. 1, No.3, pp. 289-300, Sept., 2003
  29. H. S. Park and S. K. Oh, 'Multi-FNN Identification Based on HCM Clustering and Evolutionary Fuzzy Granulation', International journal of Control, Automations, and Systems, Vol. 1, No.2, pp. 194-202, June, 2003
  30. S. K. Oh, W. Pedrycz and H. S. Park, 'Implicit Rule-based Fuzzy-Neural Networks Using the Identification Algorithm of GA Hybrid Scheme Based on Information Granulation', Advanced Engineering Informatics, Vol. 16, No.4, pp 247-263, Oct. 2002 https://doi.org/10.1016/S1474-0346(03)00016-8
  31. S. K. Oh, W. Pedrycz and H. S. Park, 'Genetically Optimized Multi-layer Fuzzy Polynomial Neural Networks: Analysis and Design', Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 10, No.1, pp. 1-15, 2006