A hierarchical fuzzy-clustering approach to fuzzy modeling

Fuzzy Sets and Systems

Volumn 150, Issue 2, 2005, Pages 245-266

  Tsekouras, George ^a   Sarimveis, Haralambos ^b   Kavakli, Evagelia ^a   Bafas, George ^b  

^a UNIVERSITY OF THE AEGEAN   (Greece)

^b NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Fuzzy basis functions; Nearest neighbor clustering; Optimal fuzzy clustering; Ordinary fuzzy partitions

Indexed keywords

ALGORITHMS; DATA REDUCTION; ERROR ANALYSIS; ESTIMATION; MEMBERSHIP FUNCTIONS; STATE SPACE METHODS;

FUZZY BASIS FUNCTIONS; NEAREST NEIGHBOR CLUSTERING; OPTIMAL FUZZY CLUSTERING; ORDINARY FUZZY PARTITIONS;

FUZZY SETS;

EID: 11244351633     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2004.04.013     Document Type: Article

References (44)

1. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. 1981;Plenum Press, New York
2. Box G.E.P., Jenkins G.M. Time Series Analysis, Forecasting and Control. 1970;Holden Day, San Francisco, CA
3. Chen Y.-G. Xi J.Q., Zhang Z.-J. A clustering algorithm for fuzzy model identification. Fuzzy Sets and Systems. 98:1998;319-329
4. Chin T.C., Qi X.M. Genetic algorithms for learning the rule base of fuzzy logic controller. Fuzzy Sets and Systems. 97:1998;1-7
5. de Souza F.J., Vellasco M.M.R., Pacheco M.A.C. Hierarchical neuro-fuzzy quadtree models. Fuzzy Sets and Systems. 130:2002;189-205
6. Dickerson J.A., Kosko B. Fuzzy function approximation with ellipsoidal rules. IEEE Trans. System Man and Cybernet. SMC-26(4):1996;542-560
7. Evsukoff A., Branco A.C.S., Galichet S. Structure identification and parameter optimization for non-linear fuzzy modeling. Fuzzy Sets and Systems. 132(2):2002;173-188
8. Gomez-Skarmeta A.F., Delgado M., Vila M.A. About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets and Systems. 106:1999;179-188
9. Hirota K., Pedrycz W. Directional fuzzy clustering and its application to fuzzy modeling. Fuzzy Sets and Systems. 80:1996;315-326
10. Ishibuchi H., Nozaki K., Yamamoto N., Tanaka H. Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Systems. 3(3):1995;260-270
11. Jang J.S.R. ANFIS. Adaptive-network-based fuzzy inference systems IEEE Trans. System Man Cybernet. 23(3):1993;665-685
12. Kaymak U., Setnes M. Fuzzy clustering with volume prototypes and adaptive cluster merging. IEEE Trans. Fuzzy Systems. 10(6):2002;705-712
13. Kim D., Kim C. Forecasting time series with genetic fuzzy predictor ensemble. IEEE Trans. Fuzzy Systems. 5(4):1997;523-535
14. Kim E., Lee H., Park M., Park M. A simply identified Sugeno-type fuzzy model via double clustering. Inform. Sci. 110:1998;25-39
15. Kim E., Park M., Ji S., Park M. A new approach to fuzzy modeling. IEEE Trans. Fuzzy Systems. 5(3):1997;328-337
16. Klawonn F., Kruse R. Constructing a fuzzy controller from data. Fuzzy Sets and Systems. 85:1997;177-193
17. Kroll A. Identification of functional fuzzy models using multidimensional reference fuzzy sets. Fuzzy Sets and Systems. 80:1996;149-158
18. Kukolj D. Design of adaptive Takagi-Sugeno-Kang fuzzy models. Appl. Soft Comput. 2:2002;89-103
19. Lin Y., Cunningham G.A. A new approach to fuzzy-neural modeling. IEEE Trans. Fuzzy Systems. 3:1995;190-197
20. Linkens D.A., Chen M.Y. Input selection and partition validation for fuzzy modelling using neural network. Fuzzy Sets and Systems. 107:1999;299-308
21. Nakamori Y., Ryoke M. Identification of fuzzy prediction models through hyper-ellipsoidal clustering. IEEE Trans. System Man Cybernet. SMC-24(8):1994;1153-1173
22. Nie J. Fuzzy control of multivariable nonlinear servomechanisms with explicit decoupling scheme. IEEE Trans. Fuzzy Systems. 5(2):1997;304-311
23. Nie J., Linkens D. Fuzzy-Neural Control: Principles, Algorithms and Applications. 1995;Prentice-Hall, Englewood Cliffs, NJ
24. Nozaki K., Ishibuchi H., Tanaka H. Adaptive fuzzy rule-based classification systems. IEEE Trans. Fuzzy Systems. 4(3):1996;238-250
25. Nozaki K., Ishibuchi H., Tanaka H. A simple but powerful method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems. 86:1997;251-270
26. Oh S.K., Pedrycz W. Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems. Fuzzy Sets and Systems. 115(2):2000;205-230
27. Pedrycz W. Numerical and application aspects of fuzzy relational equations. Fuzzy Sets and Systems. 11:1983;1-18
28. Pedrycz W. An identification algorithm in fuzzy relational systems. Fuzzy Sets and Systems. 13:1984;153-167
29. Pedrycz W., Reformat M. Rule-based modeling of nonlinear relationships. IEEE Trans. Fuzzy Systems. 5(2):1997;256-269
30. Setnes M. Supervised fuzzy clustering for rule extraction. IEEE Trans. Fuzzy Systems. 8(4):2000;416-424
31. Sugeno M., Kang G.T. Structure identification of a fuzzy model. Fuzzy Sets and Systems. 28:1988;15-33
32. Sugeno M., Tanaka K. Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets and Systems. 42:1991;315-334
33. Sugeno M., Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Systems. 1(1):1993;7-31
34. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Systems Man Cybernet. SMC-15(1):1985;116-132
35. Tong R.M. Synthesis of fuzzy models for industrial processes. Internat. J. Gen. Syst. 4:1978;143-162
36. Tong R.M. The evaluation of fuzzy models derived from experimental data. Fuzzy Sets and Systems. 4:1980;1-12
37. Wang L., Langari R. Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques. IEEE Trans. Fuzzy Systems. 3:1995;454-458
38. Wang L.X. Adaptive Fuzzy Systems and Control. 1994;Prentice-Hall, Englewood Cliffs, NJ
39. L.X. Wang, J.M. Mendel, Back-propagation fuzzy systems as nonlinear dynamic system identifiers, Proc. IEEE Internat. Conf. Fuzzy Systems, San Diego, 1992, pp. 1409-1418.
40. Wang L.X., Mendel J.M. Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Trans. Neural Networks. 3(5):1992;807-814
41. Wang L.X., Mendel J.M. Generating fuzzy rules by learning from examples. IEEE Trans. System Man and Cybernet. 22(6):1992;1414-1427
42. Wong C.-C., Her S.-M. A self-generating method for fuzzy system design. Fuzzy Sets and Systems. 103:1999;13-25
43. Xie L.X., Beni G. A validity measure for fuzzy clustering. IEEE Trans. PAMI. PAMI-13:1991;841-847
44. Xu C.W., Lu Y.Z. Fuzzy model identification and self-learning for dynamic systems. IEEE Trans. System Man Cybernet. SMC-17:1987;683-689