Transactions on Control, Automation and Systems Engineering

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지

Advanced Polynomial Neural Networks Architecture with New Adaptive Nodes

  • Oh, Sung-Kwun (School of Electrical, Electronics and Information Engi-neering, Wonkwang University) ;
  • Kim, Dong-Won (School of Electrical, Electronics and Information Engi-neering, Wonkwang University) ;
  • Park, Byoung-Jun (School of Electrical, Electronics and Information Engi-neering, Wonkwang University) ;
  • Hwang, Hyung-Soo (School of Electrical, Electronics and Information Engi-neering, Wonkwang University)
  • Published : 2001.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

Keywords

References

  1. IEEE Trans. On Systems, Man and Cybernetics v.SMC-1 Polynomial theory of complex systems A. G. Ivahnenko
  2. Inductive Learning Algorithms for Complex Systems Modeling A. G. Ivahnenko;H. R. Madala
  3. European Journal of Operational Research v.93 Analyzing mathematical models with inductive learning networks D. M. Steiger;R. Sharda
  4. Soil & Tillage Research v.49 Use of soil penetration resistance and group method of data handling to improe soil water retention estimates Ya. Pachepsky;W. Rawls;D. Gimenez;J. P. C. Watt
  5. Self-organizing Methods in Modeling: GMDH Type Algorithms The GMDHalgorithm S. J. Farlow(ed.)
  6. IEEE Trans. Fuzzy Systems v.1 no.1 A fuzzy-logic-based approach to qualitative modeling M. Sugeno;T. Yasukawa
  7. Fuzzy sets and Systems v.57 A review and comparison of six reasoning methods H. Nakanishi;I. B. Turksen;M. sugeno
  8. Fuzzy Sets and Systems v.115 no.2 Identification of fuzzy systems by means of an Auto-Tuning algorithm and its application to nonlinear systems S. K. Oh;W. Pedrycz
  9. The Third Asian Fuzzy Systems Symposium(AFSS) Fuzzy identification by means of Auto - Tuning algorithm and weighting factor C. S. Park;S. K. Oh;W. Pedrycz
  10. Seventh IFSA World Congress Fuzzy polynomial neural networks-based architecture and its application to nonlinear process systems S. K. Oh;T. C. Ahn;W. Pedrycz
  11. Journal of Control, Automation and Systems Engineering v.6 no.3 The design of optimal Fuzzy-Neural networks architecture by means of GA and an aggregate weighted performance index S. K. Oh;K. C. Yoon;H. K. Kim
  12. IEEE Trans. Fuzzy Sysems v.5 no.3 A new approach to fuzzy modeling E. T. Kim;M. K. Park;S. H. Ji;M. Park
  13. Information Sciences v.110 A simple identified Sugeno-type fuzzy model via double clustering E. Kim;H. Lee;M. Park;M. Park
  14. Fuzzy Sets and Systems v.106 About the use of fuzzy clustering techniques for fuzzy model identification A. F. Gomez-Skarmeta;M. Delgado;M. A. Vila
  15. Time series analysis : forecasting and control G. E. P. Box;F. M. Jenkins
  16. IEEE Trans on Systems, Man, Cybenetics v.SMC-17 no.4 Fuzzy model identification self-learning for dynamic system C.W. Xu;Y. Zailu
  17. Fuzzy Sets and Systems v.13 An identification algorithm in fuzzy relational system W. Pedrycz
  18. IEEE Trans. Fuzzy Systems v.3 no.2 A new appraoch to fuzzy-neural modeling Y. Lin;G. A. Cunningham Ⅲ