On line learning fuzzy rule-based system structure from data streams

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2008, Pages 915-922

  Angelov, Plamen ^a   Zhou, Xiaowei ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FAULT DETECTION; FEATURE EXTRACTION; FUZZY RULES; FUZZY SETS; FUZZY SYSTEMS; REAL TIME SYSTEMS; SENSITIVITY ANALYSIS; SOLUTE TRANSPORT;

BASE STRUCTURES; CAR INDUSTRIES; CONTROL PROBLEMS; DATA DENSITIES; DATA SAMPLES; DATA STREAMS; LEARNING CAPABILITIES; LINE MONITORING; MECHANISM OF FORMATIONS; NEW APPROACHES; ON-LINE LEARNINGS; RULE-BASED SYSTEMS; SYSTEM PARAMETERS; SYSTEM STRUCTURES;

FUZZY LOGIC;

EID: 55349130665     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2008.4630479     Document Type: Conference Paper

References (32)

1. L. Kuncheva, Fuzzy Classifiers, Physica-Verlag. 2000, ISBN 3-7008-1298-6.
2. D. Nauck, R. Kruse, A Neuro-fuzzy method to learn fuzzy classification rules from data, Fuzzy Sets and Systems, Vol. 89, pp. 277-288, 1997.
3. K. J. Cios, W. Pedricz, R.W. Swinarski (1998) Data Mining Methods for Knowledge Discovery, Boston, MA, USA: Kluwer Academic Press.
4. J. A. Roubos, M. Setnes (2001). Compact and transparent fuzzy models and classifiers through iterative complexity reduction. IEEE Transactions on fuzzy systems, 9. 516-524.
5. R. Babuska, Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston, USA. 1998.
6. P. Angelov, C. Xydeas, Fuzzy Systems Design: Direct and Indirect Approaches, Soft Computing, special issue on New Trends in the Fuzzy Modelling part L Novel Approaches, vol. 10 (9), July 2006, pp.836-849.
7. X. Zhou, P. Angelov, "Autonomous Visual Self-localization in Completely Unknown Environment using Evolving Fuzzy Rule-based Classifier", Proc. IEEE Intern. Conf. on Comput. Intelligence Applic. for Defense and Security, Honolulu, USA, 1-5 April 2007, pp. 131-138.
8. J. J. Macias-Hernandez, P. Angelov, X. Zhou, Soft Sensor for Predicting Crude Oil Distillation Side Streams using Takagi-Sugeno Evolving Fuzzy Models, Proc. 2007 IEEE Intern. Conf. on Systems, Man, and Cybernetics, 7-10 Oct., 2007. Montreal, Canada, ISBN 1-4244-0991-8/07, pp.3305-3310.
9. A. Elgammal, R. Suraiswami, D. Harwood, and L. Davis, "Background and Foreground modeling using non-parametric Kernel Density Estimation for visual surveillance KDE", Proc. 2002 IEEE Vol. 90 (7), pp. 1151-1163.
10. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction. Heidelberg, Germany: Springer Verlag, 2001.
11. D. Specht, "A general regression neural network", IEEE Transactions on Neural Networks, vol. 2 , No 6, pp. 568-576, 1991.
12. R. R. Yager, D. P. Filev, "Learning of fuzzy rules by mountain clustering, Proc. SPIE Conf. on Appl. of Fuzzy Logic Technology, Boston. MA, USA, pp. 246-254, 1993.
13. S. L. Chiu, "Fuzzy model identification based on cluster estimation". J. of Intel. and Fuzzy Syst. vol. 2, pp. 267-278, 1994.
14. P. Angelov, "An Approach for Fuzzy Rule-base Adaptation using On-line Clustering", International Journal of Approximate Reasoning, Vol. 35 , No 3, March 2004, pp. 275-289.
15. P. Angelov, D. Filev, "An Approach to On-line Identification of Takagi-Sugeno Fuzzy Models", IEEE Transactions on System, Man, and Cybernetics, part B - Cybernetics, vol.34, No1, 2004, pp.484-498. ISSN 1094-6977.
16. P. Angelov, X. Zhou, "Evolving fuzzy systems from data streams in Real time," In Proc. 2006 International Symposium on Evolving Fuzzy Systems, Ambleside, Lake District, UK pp. 29-35, IEEE Press.
17. P. Angelov, "Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems". Heidelberg, Germany: Springer, 2002.
18. nd IFAC Workshop on Advanced Fuzzy/Neural Control,16-17 September 2004, Oulu, Finland, pp.67-72.
19. P. Angelov, X. Zhou, F. Klawonn, "Evolving Fuzzy Rule-based Classifiers", In Proc. 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, CIISP 2007, Honolulu, HI, USA, 1-5 April 2007, pp.220-225.
20. P. Angelov, E. Lughofer. P. E. Klement, Two Approaches for Data-Driven Design of Evolving Fuzzy Systems: eTS and FLEXFIS, The 2005 North American Fuzzy Information Processing Society Ann. Conf., June 21-25 2005, Ann Arbor, Michigan, USA, pp.31-35.
21. P. Angelov, R. Ramezani, X. Zhou, Autonomous Novelty Detection and Object Tracking in Video Streams using Evolving Clustering and Takagi-Sugeno type Neuro-Fuzzy System, 2008 World Congress on Comput. Intel., WCCI-2008, Hong Kong, June 2008, to appear.
22. R. Yager, D. Filev, R. R. Yager, D. P. Filev, Essentials of Fuzzy Modeling and Control, NY: John Wiley, 1994.
23. P. Angelov, Machine Learning (Collaborative Systems) UK patent application, GB-0621734.3, 1 November 2006.
24. G. Smits, A. Kordon, E. Jordaan . C. Vladislavleva, and M. Kotanchek, Variable Selection in Industrial Data Sets Using Pareto Genetic Programming, In: Yu T., R. Riolo, B. Worzel (Eds): Genetic Programming Theory & Practice III. Springer, NY, pp. 79-92, 2006.
25. P. Angelov, D. Filev, Simpl_eTS: A Simplified Method for Learning Evolving Takagi-Sugeno Fuzzy Models, The 2005 IEEE International Conference on Fuzzy Systems FUZZ-IEEE, Reno, Las Vegas, USA, 22-25 May 2005, pp.1068-1073.
26. P. Angelov, A. Kordon, X. Zhou, Evolving Fuzzy Inferential Sensors for Process Industry, 3rd Int. Workshop on Genetic & Evolving Fuzzy Syst., 4-7 March, 2008, Witten-Bomerholz, Germany, to appear.
27. G. A. Carpenter and S. Grossberg, "Adaptive Resonance Theory (ART)," in The Handbook of Brain Theory and Neural Networks, M. A. Arbib, Ed. Cambridge, MA: MIT Press, 1995, pp. 79-82.
28. R. Gray, "Vector quantization," IEEE ASSP Magazine, pp. 4-29, 1984.
29. E. Lughofer, E. Huellermeier, and E. Klement, "Improving the interpretability of data-driven evolving fuzzy systems," in Proceedings of EUSFLAT 2005, Barcelona, Spain, 2005, pp. 28-33.
30. M. Everett, P. Angelov, EvoMap: On-Chip Implementation of Intelligent Information Modelling using EVOlving MAPping, Tech. Report, 2005, Lancaster University, Lancaster, UK, pp.1-15.
31. J. S. R. Jang, ANFIS: Adaptive Network-based Fuzzy Inference Systems, IEEE Trans. on Systems, Man & Cybernetics, part B -Cybernetics, vol. 23, No 3, 1993, pp. 665-685.
32. N. Kasabov and Q. Song, DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction, IEEE Trans. on Fuzzy Systems, vol.10, No. 2, 2002, pp. 144-154.