Information granulation as a basis of fuzzy modeling

Journal of Intelligent and Fuzzy Systems

Volumn 18, Issue 2, 2007, Pages 123-148

  Kim, Euntai ^a   Pedrycz, Witold ^b,c  

^a Yonsei University   (South Korea)

^b UNIVERSITY OF ALBERTA   (Canada)

^c SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Directional aspects of clustering; Function approximation; Fuzzy c means; Fuzzy clustering; Fuzzy granular modeling; Genetic algorithm; Information granulation

Indexed keywords

COMPUTER SIMULATION; GENETIC ALGORITHMS; INFORMATION ANALYSIS; PARAMETER ESTIMATION; SENSITIVITY ANALYSIS;

DIRECTIONAL ASPECTS OF CLUSTERING; FUNCTION APPROXIMATION; FUZZY C-MEANS; FUZZY GRANULAR MODELING; INFORMATION GRANULATION;

FUZZY CLUSTERING;

EID: 34249906133     PISSN: 10641246     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. M.R. Anderberg, Cluster Analysis for Applications, Academic Press: New York, 1973.
2. T. Back, U. Hammel and H. Schwefel, Evolutionary computation: comments on the history and current state, IEEE Trans. Evolutionary Computation 1(1) (1997), 3-17.
3. J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithm, Plenum Press, New York, 1981.
4. G.E.P. Box and G.M. Jenkins, Time Series Analysis, Forecasting and Control, San Francisco: Holden Day, 1970.
5. R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification, John Wiley: NY, 2001.
6. W.A. Farag, V.H. Quintana and G. Lambert-Torres, A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems, IEEE Trans. on Neural Networks 9(5), (Sep. 1998), 756-767.
7. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley: MA, 1989.
8. J. Gonzalez, I. Rojas, H. Pomares, J. Ortega and A. Prieto, A new clustering technique for function approximation, IEEE Trans. on Neural Networks 13(1) (2002), 132-142.
9. K. Hirota and W. Pedrycz, D-fuzzy clustering, Pattern Reco. Lett. 16 (1995), 193-200.
10. R.B. Holstien, Artificial Genetic Adaptation in Computer Control Systems, PhD Thesis, Dept. of Computer and Communication Sciences, Univ. of Michigan, Ann Arbor, 1971.
11. M. Joo, S. Wu, J. Lu and H.L. Toh, Face recognition with radial basis function (RBF) neural networks, IEEE Trans, on Neural Networks 13(3) (2002), 697-710.
12. E. Kim, M. Park, S. Ji and M. Park, A new approach to fuzzy modeling, IEEE Trans. Fuzzy Systems 5(3) (Aug. 1997), 328-337.
13. E. Kim, M. Park, S. Kim and M. Park, A transformed input-domain approach to fuzzy modeling, IEEE Trans. Fuzzy Systems 6(4) (Nov. 1998), 596-604.
14. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolutionary Programs, (3rd ed.), Springer-verlag: Berlin, 1996.
15. B.-J. Park, W. Pedrycz and S.-K. Oh, Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling, IEEE Trans. on Fuzzy Systems 10(5) (Oct. 2002), 607-621.
16. S.-K. Oh, W. Pedrycz and H.S. Park, Genetically optimized fuzzy polynomial neural networks, IEEE Trans. on Fuzzy Systems 14(1) (2006), 125-144.
17. W. Pedrycz, Conditional fuzzy clustering in the design of radial basis function neural networks, IEEE Trans. Neural Networks 9(4) (1998), 601-612.
18. W. Pedrycz and K.-C. Kwag, Linguistic Models as a Framework of User-Centric System Modeling, IEEE Trans. on Sys., Man, and Cyber. Part A - Sys. and Humans (2006), in press.
19. W. Pedrycz, Conditional fuzzy C-Means, Pattern Recognition Letters 17 (1996), 625-632.
20. N.R. Pal, K. Pal, J.M. Keller and J.C. Bezdek, A Possibilistic Fuzzy c-Means Clustering Algorithm, IEEE Trans. on Fuzzy Systems 13(4) (Aug. 2005), 517-530.
21. M. Sugeno and T. Yasukawa, A fuzzy-logic-based approach to qualitative modeling, IEEE Trans. Fuzzy Systems 1(1) (Feb. 1993), 7-31.