Online data-driven fuzzy clustering with applications to real-time robotic tracking

IEEE Transactions on Fuzzy Systems

Volumn 12, Issue 4, 2004, Pages 516-523

  Liu, Peter X ^a   Meng, Max Q H ^b  

^a CARLETON UNIVERSITY   (Canada)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; FUZZY CONTROL; MATHEMATICAL MODELS; ROBOTICS; TRACKING (POSITION);

DATA CLUSTERING; FUZZY THEORY; MAXIMUM ENTROPY PRINCIPLE; TARGET TRACKING;

FUZZY SETS;

EID: 4344630420     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2004.832521     Document Type: Article

References (51)

1. D. Hujic, E. A. Croft, G. Zak, R. G. Fenton, J. K. Mills, and B. Benhabib, "The robotic interception of moving objects in industrial settings: Strategy development and experiment," IEEE/ASME Trans. Mechatron., vol. 3, pp. 225-239, Feb. 1998.
2. P. K. Allen, A. Timcenko, B. Yoshimi, and P. Michelman, "Automated tracking and grasping of a moving object with a robotic hand-eye system," IEEE Trans. Robot. Automat., vol. 9, pp. 152-165, Jan. 1993.
3. R. C. Luo, "Target tracking using hierarchical grey-fuzzy motion decision-making method," IEEE Trans. Syst., Man, Cybern. A, vol. 31, pp. 179-186, June 2001.
4. F-B Duh and C-T Lin, "Tracking a maneuvering target using neural fuzzy network," IEEE Trans. Syst., Man, Cybern. B, vol. 34, pp. 16-33, Feb. 2004.
5. L. Chin, "Application of neural networks in target tracking data fusion," IEEE Trans. Aerospace Electron. Syst., vol. 30, pp. 281-287, Feb. 1994.
6. P. J. Pacini and B. Kosko, "Adaptive fuzzy systems for target tracking," Intell. Syst. Eng., vol. 1, no. 1, pp. 3-21, 1992.
7. S. Rovetta and R. Zunino, "A multiprocessor-oriented visual tracking system," IEEE Trans. Ind. Electron., vol. 46, pp. 842-850, Oct. 1999.
8. P. Payeur, H. Le-Hui, and C. M. Gosselin, "Trajectory prediction for moving objects using artificial neural networks," IEEE Trans. Ind. Electron., vol. 42, pp. 147-158, Feb. 1995.
9. K. C. C. Chan, V. Lee, and H. Leung, "Radar tracking for air surveillance in a stressful environment using a fuzzy-gain filter," IEEE Trans. Fuzzy Syst., vol. 5, pp. 80-89, Feb. 1997.
10. S. McGinnity and G. W. Irwin, "Fuzzy logic approach to maneuvering target tracking," Proc. Inst. Elect. Eng. Radar, Sonar, and Navigation, vol. 145, no. 6, pp. 337-341, 1998.
11. P. J. Pacini and B. Kosko, "Adaptive fuzzy systems for target tracking," Intell. Syst. Eng., vol. 1, no. 1, pp. 3-21, 1992.
12. N. P. Papanikolopoulos, P. K. Khosla, and T. Kanade, "Visual tracking of a moving target by a camera mounted on a robot: A combination of control and vision," IEEE Trans. Robot. Automat., vol. 9, pp. 14-35, Jan. 1993.
13. A. J. Koivo and N. Houshangi, "Real-time vision feedback for servoing robotic manipulator with self-tuning controller," IEEE Trans. Syst., Man, Cybern., vol. 21, pp. 134-142, Feb. 1991.
14. A. Elnagar and K. Gupta, "Motion prediction of moving objects based on autoregressive model," IEEE Trans. Syst., Man, Cybern. A, vol. 28, pp. 803-810, Dec. 1998.
15. P. P. Angelov, Evolving Rule-Based Models. New York: Physica-Verlag, 2002.
16. E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis of fuzzy controllers," Int. J. Man-Machine Studies, vol. 7, pp. 1-13, 1975.
17. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its application to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. SMC-15, pp. 116-132, Feb. 1985.
18. W. Pedrycz, "An identification algorithm in fuzzy relational systems," Fuzzy Sets Syst., vol. 13, pp. 153-167, 1984.
19. L.-X. Wang and J. M. Mendel, "Generating fuzzy roles by learning from examples," IEEE Trans. Syst., Man, Cybern., vol. 22, pp. 1414-1427, Dec. 1992.
20. J. S. R. Jang, "ANFIS: Adaptive network-based fuzzy inference systems," IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 665-685, Aug. 1993.
21. R. Yager and D. P. Fileu, "Learning of fuzzy rules by mountain clustering," in Proc. SPIE Applicat. Fuzzy Logic Technol., B. Bosacchi and J. C. Bezdek, Eds., 1993, vol. 2061, pp. 246-254.
22. G. Beni and X. Liu, "A least biased fuzzy clustering method," IEEE Trans. Pattern Anal. Machine Intell., vol. 16, pp. 954-960, Sept. 1994.
23. S. L. Chiu, "Fuzzy model identification based on cluster estimation," J. Intell. Fuzzy Syst., vol. 2, pp. 267-278, 1994.
24. C. T. Sun, "Rule-based structure identification in an adaptive-network-based fuzzy inference system," IEEE Trans. Fuzzy Syst., vol. 2, pp. 64-73, Feb: 1994.
25. K. Shimojima, T. Fukuda, and Y. Hasegawa, "Self-tuning fuzzy modeling with adaptive membership function, rules and hierarchical structure based on genetic algorithm," Fuzzy Sets Syst., vol. 71, pp. 295-309, 1995.
26. F. Herrera, M. Lozano, and J. L. Verdegay, "Generating fuzzy rules from examples using genetic algorithms," in Fuzzy Logic and Soft Computing, B. Bouchon-Meunier, R. R. Yager, and L. A. Zadeh, Eds. Singapore: World Scientific, 1995, pp. 11-20.
27. B. Carse, T. C. Fogarty, and A. Munro, "Evolving fuzzy rule-based controllers using GA," Fuzzy Sets Syst., vol. 80, pp. 273-294, 1996.
28. C. F. Juang and C. T. Lin, "An online self-constructing neural fuzzy inference network and its application," IEEE Trans. Fuzzy Syst., vol. 6, pp. 12-32, Feb. 1998.
29. M. Setnes, R. Babuska, and H. B. Berbruggen, "Rule-based modeling: Precision and transparency," IEEE Trans. Syst., Man, Cybern. C, vol. 28, pp. 165-169, Jan. 1998.
30. L. Castillo, A. Gonzalez, and R. Perez, "Including a simplicity criterion in the selection of the best role in a genetic fuzzy learning algorithm," Fuzzy Sets Syst., vol. 120, no. 2, pp. 309-321, 2001.
31. 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, pp. 144-154, Feb. 2002.
32. P. Angelov and R. Buswell, "Identification of evolving fuzzy rule-based models," IEEE Trans. Fuzzy Syst., vol. 10, pp. 667-677, Oct. 2002.
33. A. E. Gaweda and J. M. Zurada, "Data-driven linguistic modeling using relational fuzzy rules," IEEE Trans. Fuzzy Syst., vol. 11, pp. 121-134, Feb. 2003.
34. S. Guiasu and A. Shenitzer, "Maximum entropy principle," Math. Intell., vol. 7, no. 1, pp. 42-48, 1985.
35. E. T. Jaynes, "Information theory and statistical mechanics I," Phys. Rev., vol. 106, no. 4, pp. 620-630, 1957.
36. J. Uffink, "Can the maximum entropy principle be explained as a consistency requirement?," Stud. History Philo. Mod. Phys., vol. 26, no. 3, pp. 223-261, 1995.
37. P. Angelov and R. Buswell, "Identifiation of evolving fuzzy rule-based models," IEEE Trans. Fuzzy Syst., vol. 10, pp. 667-677, Oct. 2002.
38. F-J. Lin, C.-H. Jin, and P-H. Shen, "Self-constructing fuzzy neural netowrk speed conroller for permanent-magnet synchronous motor drive," IEEE Trans. Fuzzy Syst., vol. 9, pp. 751-759, Dec. 2001.
39. G. C. Goodwin and K. S. Sin, Adaptive Filtering Prediction and Control. Upper Saddle River, NJ: Prentice-Hall, 1984.
40. W. R. Greiff and J. M. Ponte, "The maximum entropy approach and probabilistic IR models," ACM Trans. Inform. Syst., vol. 18, no. 3, pp. 246-287, 2000.
41. P. Klan and G. Darbellay, "An information-theoretic adaptive method for time series forecasting," Neural Network World, vol. 7, no. 2, pp. 227-238, 1997.
42. C. E. Shannon and C., "A mathematical theory of communication," Bell. Syst. Techn. J., vol. 27, pp. 379-423, 1948.
43. E. T. Jaynes, "Information theory and statistical mechanics II," Phys. Rev., vol. 108, pp. 171-190, 1975.
44. A. Golan and J. M. Perloff, "Comparison of maximum entropy and higher-order entropy estimators," J. Economet., vol. 107, no. 1-2, pp. 195-211, 2002.
45. K. Rose, E. Gurewitz, and G. C. Fox, "Statistical mechanics and phase transitions in clustering," Phys. Rev. Lett., vol. 65, no. 8, pp. 945-948, 1990.
46. K. Rose, "Deterministic annealing for clustering, compression, classification, regressions, and related optimization problems," Proc. IEEE, vol. 86, pp. 2210-2239, Dec. 1998.
47. H-L Eng and K-K Ma, "Unsupervised image object segmentation over compressed domain," in Proc. IEEE Int. Conf. Image Processing, vol. 3, 2000, pp. 758-761.
48. A. Baraldi and P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition - Part I," IEEE Trans. Syst., Man, Cybern. B, vol. 29, pp. 778-785, Dec. 1999.
49. A. Baraldi, "A survey of fuzzy clustering algorithms for pattern recognition - Part II," IEEE Trans. Syst., Man, Cybern. B, vol. 29, pp. 786-801, Dec. 1999.
50. M. Mackey and L. Glass, "Radial basis functions for multivariable interpolation: A review," Science, vol. 197, pp. 287-289.
51. F. Gers, D. Eck, and J. Schmidhuber, Applying LSTM to Time Series Predictable Through Time-Window Approaches. New York: Springer-Verlag, 2001.