Supervised hierarchical clustering in fuzzy model identification

IEEE Transactions on Fuzzy Systems

Volumn 19, Issue 6, 2011, Pages 1163-1176

  Hartmann, Benjamin ^a   Bänfer, Oliver ^a   Nelles, Oliver ^a   Sodja, Anton ^b   Teslić, Luka ^b   Škrjanc, Igor ^b  

^a UNIVERSITY OF SIEGEN   (Germany)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Fuzzy clustering; fuzzy model identification; hierarchical tree construction; supervised learning

Indexed keywords

BENCH-MARK PROBLEMS; FUZZY MODEL IDENTIFICATION; FUZZY MODELS; GLOBAL MODELS; HIER-ARCHICAL CLUSTERING; HIERARCHICAL ALGORITHM; HIERARCHICAL CLUSTERING ALGORITHMS; HIERARCHICAL TREE-CONSTRUCTION; HIGH FLEXIBILITY; INTERPRETABILITY; LOCAL MODEL; PARTITIONING ALGORITHMS; SPACE CLUSTERING; TERMINATION CRITERIA;

BENCHMARKING; FORESTRY; FUZZY CLUSTERING; HEURISTIC ALGORITHMS; SUPERVISED LEARNING; TREES (MATHEMATICS);

CLUSTERING ALGORITHMS;

ALGORITHMS; FUZZY LOGIC; MATHEMATICAL MODELS; MATHEMATICS;

EID: 82455221063     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2011.2164256     Document Type: Article

References (55)

1. M. Sugeno and G. T. Kang, "Structure identification of fuzzy model" Fuzzy Sets Syst., vol. 28, no. 1, 1988.
2. J. Abonyi and R. Babuŝka, "Local and global identification and interpretation of parameters in Takagi-Sugeno fuzzy models" in Proc. IEEE Conf. Fuzzy Syst., San Antonio, TX, 2000, pp. 835-840.
3. T. A. Johansen and R. Babuŝka, "Multiobjective identification of Takagi-Sugeno fuzzy models" IEEE Trans. Fuzzy Syst., vol. 11, no. 6, pp. 847-860, Dec. 2003.
4. J. Yen, L. Wang, and C. W. Gillespie, "Improving the interpretability of TSK fuzzy models by combining global learning and local learning" IEEE Trans. Fuzzy Syst., vol. 6, no. 4, pp. 530-537, Aug. 1998.
5. J. Abonyi, R. Babuŝka, and F. Szeifert, "Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models" IEEE Trans. Syst., Man, Cybern. B, vol. 32, no. 5, pp. 612-621, Oct. 2002.
6. R. Babuŝka, Fuzzy Modeling for Control. Boston, MA: Kluwer, 1998.
7. R. Babuŝka and H. B. Verbruggen, "An overview of fuzzy modeling for control" Control Eng. Practice, vol. 4, no. 11, pp. 1593-1606, 1996.
8. R. Babuŝka, J. A. Roubos, and H. B. Verbruggen, "Identification ofMIMO systems by input-output T-S fuzzy models" in Proc. IEEE World Congr. Comput. Intell., 1998, pp. 657-662.
9. M. Sugeno and T.Yasukawa, "Afuzzy-logic-based approach to qualitative modeling" IEEE Trans. Fuzzy Syst., vol. 1, no. 1, pp. 7-31, Feb. 1993.
10. E. Kim, M. Park, S. Ji, and M. Park, "A transformed input-domain approach to fuzzy modeling" IEEE Trans. Fuzzy Syst., vol. 6, no. 4, pp. 596-604, Nov. 1998.
11. Q. Gan and C. J. Harris, "Fuzzy local linearization and logic basis function expansion in nonlinear system modeling" IEEE Trans. Syst., Man, Cybern. B, vol. 29, no. 4, pp. 559-565, Jan. 1999.
12. Y. Shi, R. Eberhart, and Y. Chen, "Implementation of evolutionary fuzzy systems" IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 109-119, Apr. 1999.
13. C. K. Lin and S. D.Wang, "Fuzzy system identification using an adaptive learning rule with terminal attractors" J. Fuzzy Sets Syst., pp. 343-352, 1999.
14. S. J. Kang, C. H. Woo, H. S. Hwang, and K. B. Woo, "Evolutionary design of fuzzy rule base for nonlinear system modeling and control" IEEE Trans. Fuzzy Syst., vol. 8, no. 1, pp. 37-45, Feb. 2000.
15. M. R. Emani, I. B. Turksen, and A. A. Goldenberg, "Development of a systematic methodology of fuzzy logic modeling" IEEE Trans. Fuzzy Syst., vol. 6, no. 3, pp. 46-61, Aug. 1998.
16. B. Wu and X. Yu, "Fuzzy modelling and identification with genetic algorithm based learning" Fuzzy Sets Syst., vol. 113, pp. 352-365, 2000.
17. Y. P. Huang and S.-F. Wang, "Designing a fuzzy model by adaptive macroevolution genetic algorithms" Fuzzy Sets Syst., vol. 113, pp. 367-379, 2000.
18. M. Denna, G. Mauri, and A. M. Zanaboni, "Learning fuzzy rules with tabu search-an application to control" IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 295-318, Jun. 1999.
19. L. X. Wang, "Analysis and design of hierarchical fuzzy systems" IEEE Trans. Fuzzy Syst., vol. 7, no. 5, pp. 617-624, Oct. 1999.
20. O. Huwendiek andW. Brockmann, "Function approximation with decomposed fuzzy systems" Fuzzy Sets Syst., vol. 101, pp. 273-286, 1999.
21. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference systems" IEEE Trans. Syst., Man Cybern., vol. 23, no. 3, pp. 665-685, May/Jun. 1993.
22. C. T. Sun, "Rule-based structure identification in an adaptive network based fuzzy inference system" IEEE Trans. Fuzzy Syst., vol. 2, no. 1, pp. 64-73, Feb. 1994.
23. S. J. Lee and C. S. Ouyang, "A neuro-fuzzy system modeling with selfconstructing rule generation and hybrid SVD-based learning" IEEE Trans. Fuzzy Syst., vol. 11, no. 3, pp. 341-53, Jun. 2003.
24. S. Chiu, "Fuzzy model identification based on cluster estimation" J. Intell. Fuzzy Syst., vol. 2, pp. 267-278, 1994.
25. L. Breiman, C. J. Stone, and J. H. Friedman R., R. Olshen, Classification and Regression Trees. New York: Chapman & Hall, 1984.
26. L. Breiman, "Hinging hyperplanes for regression, classification, and function approximation" IEEE Trans. Inf. Theory, vol. 39, no. 3, pp. 999-1013, May 1993.
27. P. Pucar and M. Millnert, "Smooth hinging hyperplanes: A alternative to neural nets" in Proc. Eur. Control Conf. (ECC), Rome, Italy, 1995, pp. 1173-1178.
28. O. Nelles, S. Sinsel, and R. Isermann, "Local basis function networks for identification of a turbocharger" presented at the IEEE UKACC Int. Conf. Control, Exeter, U.K., Sep. 1996, pp. 7-12.
29. O. Nelles, Nonlinear System Identification. Berlin, Germany: Springer-Verlag, 2001.
30. O. Nelles, "Axes-oblique partitioning strategies for local model networks" presented at the Int. Symp. Intell. Control, Munich, Germany, Oct. 2006.
31. T.A. Johansen, R. Shorten, and R. Murray-Smith, "On the interpretation and identification of Takagi-Sugeno fuzzy models" IEEE Trans. Fuzzy Syst., vol. 8, pp. 297-313, Jun. 2000.
32. T. Takagi andM. Sugeno, "Fuzzy identification of systems and its application to modeling and control" IEEE Trans. Syst., Man, Cybern., vol. 15, no. 1, pp. 116-132, Feb. 1985.
33. D. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix" in Proc. IEEE Conf. Decis. Control, 1979, pp. 761-766.
34. I. Gath and A. B. Geva, "Unsupervised optimal fuzzy clustering" IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 773-781, Jul. 1989.
35. T. A. Johansen, "Identification of non-linear system structure and parameters using regime decomposition" Automatica, vol. 31, no. 2, pp. 321-326, 1995.
36. S. Ernst, "Hinging hyperplane trees for approximation and identification" in Proc. IEEE Conf. Decis. Control, 1998, pp. 1261-1277.
37. R. Murray-Smith and T. A. Johansen, "Local learning in local model networks" in Proc. Fourth Int. Conf. Artif. Neural Netw., Jun. 1995, pp. 40-46.
38. R. Murray-Smith, "A local model network approach to nonlinear modeling" Ph.D. dissertation, Univ. Strathclyde, Glasgow, U.K., 1994.
39. J. H. Friedman, "Multivariate adaptive regression splines (with discussion)" Ann. Statist., vol. 19, no. 1, pp. 1-141, 1991.
40. M. Melouna, D. Burkonova, T. Syrovy, and A. Vrana, "Thermodynamic dissociation constants of silychristin, silybin, silydianin and mycophenolate by the regression analysis of spectrophotometric data" Analytica Chimica Acta, vol. 486, pp. 125-141, 2003.
41. F. Hoffmann and O. Nelles, "Genetic programming for model selection of TSK-fuzzy systems" Inf. Sci., vol. 136, no. 1-4, pp. 7-28, 2001.
42. F. Hoffmann and O. Nelles, "Structure identification of TSK-fuzzy systems using genetic programming" in Proc. Inf. Process. Manage. Uncertainty Knowl.-Based Syst, Madrid, Spain, 2008, pp. 438-445.
43. F. Hoffmann, D. Schauten, and S. Hölemann, "Incremental evolutionary design of TSK fuzzy controllers" IEEE Trans. Fuzzy Syst., vol. 15, no. 4, pp. 563-577, Aug. 2007.
44. N. K. Kasabov and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction" IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-154, Apr. 2002.
45. J. S. R. Jang, "Input selection for ANFIS learning" in Proc. IEEE ICFS, New Orleans, LA, 1996, pp. 1493-1499.
46. P. P. Angelov and D. P. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models" IEEE Trans. Syst., Man, Cybern. B: Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
47. E. D. Lughofer, "Flexfis: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models" IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1393-1410, Dec. 2008.
48. C. F. Juang and C. T. Lin, "An online self-constructing neural fuzzy inference network and its applications" IEEE Trans. Fuzzy Syst., vol. 6, no. 1, pp. 12-32, Feb. 1998.
49. J. de Jeśus Rubio, "SOFMLS:Online self-organizing fuzzy modified leastsquares network" IEEE Trans. Fuzzy Syst., vol. 17, no. 6, pp. 1296-1309, Dec. 2009.
50. S. Töpfer, "Approximation of nonlinear processeswith hinging hyperplane trees" at-Automatisierungstechnik, Oldenburg, vol. 50, no. 4, pp. 147-154, 2002.
51. G. Tsekouras, H. Sarimveis, E. Kavakli, and G. Bafas, "A hierarchical fuzzy-clustering approach to fuzzy modeling" Fuzzy Sets Syst., vol. 150, no. 2, pp. 245-266, 2005.
52. Y. Jin, "Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement" IEEE Trans. Fuzzy Syst., vol. 8, no. 2, pp. 212-221, Apr. 2000.
53. J. A. Roubos and M. Setnes, "Compact fuzzy models through complexity reduction and evolutionary optimization" in Proc. IEEE Int. Conf. Fuzzy Syst., 2000, pp. 762-767.
54. B. Tutmez and Z. Hatipoglu, "Comparing two data driven interpolation methods for modeling nitrate distribution in aquifer" Ecol. Informat., vol. 5, no. 4, pp. 311-315, 2010.
55. J. Madar, J. Abonyi, and F. Szeifert, "Genetic programming for the identification of nonlinear input?output models" Ind. Eng. Chem. Res., vol. 44, no. 9, pp. 3178-3186, 2005.