International Journal of Fuzzy Logic and Intelligent Systems

  • 제6권1호
  • /
  • Pages.33-38
  • /
  • 2006
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Genetically Optimized Fuzzy Polynomial Neural Network and Its Application to Multi-variable Software Process

  • Lee In-Tae (Department of Electrical Engineering, The University of Suwon) ;
  • Oh Sung-Kwun (Department of Electrical Engineering, The University of Suwon) ;
  • Kim Hyun-Ki (Department of Electrical Engineering, The University of Suwon) ;
  • Pedrycz Witold (Department of Electrical and Computer Engineering, University of Alberta)
  • 발행 : 2006.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

키워드

참고문헌

  1. W. Pedrycz, Computational Intelligence: An Introduction, CRC Press, Florida, (1998)
  2. J.F Peters and W. Pedrycz, Computational intelligence, In Encyclopedia of Electrical and Electronic Engineering, Volume 22, (Edited by J.G. Webster). John Wiley & Sons, New York, (1999)
  3. W. Pedrycz and J.F. Peters(Editers), Computational Intelligence in Software Engineering, World Scientific, Singapore, (1998)
  4. H. Takagi and I. Hayashi, NN-driven fuzzy reasoning, Int. J. of Approximate Reasoning 5 (3), 191-212, (1991) https://doi.org/10.1016/0888-613X(91)90008-A
  5. O. Cordon, et al., 'Ten years of genetic fuzzy systems: current framework and new trends', Fuzzy Sets and Systems, Vol. 141, No.1, pp. 5-31, 2004 https://doi.org/10.1016/S0165-0114(03)00111-8
  6. S.-K. Oh and W. Pedrycz, 'Self-organizing Polynomial Neural Networks Based on PNs or FPNs : Analysis and Design', Fuzzy Sets and Systems, volume 142, pp163-198, 1 March 2004 https://doi.org/10.1016/S0165-0114(03)00307-5
  7. Z. Michalewicz, 'Genetic Algorithms + Data Structures = Evolution Programs', Springer-Verlag, Berlin Heidelberg, 1996
  8. M.R. Lyu (Editor), Handbook of Software Reliability Engineering, McGraw-Hill, (1995)
  9. S.-K. Oh and W. Pedrycz, 'Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks', Int. J. of General Systems, Vol. 32, No.3, pp. 237-250, May, 2003 https://doi.org/10.1080/0308107031000090756
  10. L. X. Wang, J. M. Mendel, 'Generating fuzzy rules from numerical data with applications',' IEEE Trans. Systems, Man, Cybern., Vol. 22, No.6, pp. 1414-1427, 1992 https://doi.org/10.1109/21.199466
  11. S. K. Oh, W. Pedrycz, and B. J. Park, 'Relation-based Neurofuzzy Networks with Evolutionary Data Granulation', Methematical and Computer Modeling, 2003
  12. S. K. Oh and W. Pedrycz, 'Fuzzy Polynomial Neuron-Based Self-Organizing Neural Networks', Int. J. of General Systems, Vol. 32, No.3, pp. 237-250, May, 2003 https://doi.org/10.1080/0308107031000090756
  13. S.-K. Oh, W. Pedrycz and T.-C. Ahn, 'Self-organizing neural networks with fuzzy polynomial neurons', Applied Soft Computing, Vol. 2, Issue IF, pp. 1-10, Aug. 2002 https://doi.org/10.1016/S1568-4946(02)00023-6
  14. T. Yamakawa, A new effective learning algorithm for a neo fuzzy neuron model, 5th IFSA World Conference, pp. 1017-1020, 1993
  15. S.-K. Oh, W. pedrycz and T.-C. Ahn, 'Self-organizing neural networks with fuzzy polynomial neurons', Applied Soft Computiong, Vol. 2, Issue IF, pp. 1-10, Aug. 2002 https://doi.org/10.1016/S1568-4946(02)00023-6