Identification of evolving fuzzy rule-based models

IEEE Transactions on Fuzzy Systems

Volumn 10, Issue 5, 2002, Pages 667-677

  Angelov, Plamen ^a,b   Buswell, Richard ^b  

^a IEEE   (United Kingdom)

^b LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

Evolving fuzzy rule based (eR) models; Fuzzy models identification; Rule base adaptation

Indexed keywords

BEHAVIOR MODEL; FAULT DETECTION; FUZZY RULE BASED MODELS; KNOWLEDGE EXTRACTION; UNSUPERVISED LEARNING;

ADAPTIVE CONTROL SYSTEMS; COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; FAILURE ANALYSIS; FUZZY SETS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; PARAMETER ESTIMATION; ROBOTICS;

KNOWLEDGE BASED SYSTEMS;

EID: 0036802284     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2002.803499     Document Type: Article

References (30)

1. R. R. Yager and D. P. Filev, "Learning of fuzzy rules by mountain clustering," in Proc. SPIE Conf. Application of Fuzzy Logic Technology, Boston, MA, 1993, pp. 246-254.
2. L. Ljung, System Identification, Theory for the User. Upper Saddle River, NJ: Prentice-Hall, 1987.
3. R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control, NY: Wiley, 1994.
4. T. A. Johanson and R. Murray-Smith, "Operating regime approach to nonlinear modeling and control," in Multiple Model Approaches to Modeling and Control, R. Murray-Smith and T. A. Johanson, Eds. Hants, U.K.: Taylor Francis, 1998, pp. 3-72.
5. J. S. R. Jang, "ANFIS: Adaptive network-based fuzzy inference systems," IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 665-685, June 1993.
6. C. K. Chiang, H.-Y. Chung, and J. J. Lin, "A self-learning fuzzy logic controller using genetic algorithms with reinforcements," IEEE Trans. Fuzzy Syst., vol. 5, pp. 460-467, 1996.
7. P. P. Angelov, V. I. Hanby, R. A Buswell, and J. A. Wright, "Automatic generation of fuzzy rule-based models from data by genetic algorithms," in Advances in Soft Computing, R. John and R. Birkenhead, Eds. New York: Springer-Verlag, 2001, pp. 31-40.
8. F. Hoffmann and G. Pfister, "Learning of a fuzzy control rule base using Messy genetic algorithms," in Studies in Fuzziness and Soft Computing, Herrera and Verdegay, Eds. Heidelberg, Germany: Physica Verlag, 1996, vol. 8, pp. 279-305.
9. P. Angelov, Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems, In the Series Studies in Fuzziness and Soft Computing. Heidelberg, Germany: Springer, Physica-Verlag, 2002, vol. 92, pp. 1-213.
10. B. Carse, T. C. Fogarty, and A. Munro, "Evolving fuzzy rule-based controllers using GA," Fuzzy Sets Syst., vol. 80, pp. 273-294, 1996.
11. P. Angelov and R. Guthke, "A GA-based approach to optimization of bioprocesses described by fuzzy rules," J. Bioprocess Eng., vol. 16, pp. 299-301, 1997.
12. 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.
13. 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, 1985.
14. W. Pedrycz, "An identification algorithm in fuzzy relational systems," Fuzzy Sets Syst., vol. 13, pp. 153-167, 1984.
15. P. P. Angelov, V. I. Hanby, and J. A. Wright, "HVAC systems simulation: A self-structuring fuzzy rule-based approach," Int. J. Arch. Sci., vol. 1, no. 1, pp. 49-58, 2000.
16. S. L. Chiu, "Fuzzy model identification based on cluster estimation," J. Intell. Fuzzy Syst., vol. 2, pp. 267-278, 1994.
17. J. Bezdek, "Cluster validity with fuzzy sets," J. Cybern., vol. 3, no. 3, pp. 58-71, 1974
18. F.-J. Lin, C.-H. Lin, and P.-H. Shen, "Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive," IEEE Trans. Fuzzy Syst., vol. 9, pp. 751-759, Oct. 2001.
19. "EUNITE: European Network on Intelligent Technologies for Smart Adaptive Systems,", Contract IST-2000-29 207, 2000.
20. R. S. Crowder, "Predicting the Mackey-Glass timeseries with Cascade-correlation learning," in Proc. of Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds. Pittsburgh, PA: Carnegie Mellon University, 1990, pp. 117-123.
21. H. R. Berenji, "A reinforcement learning-based architecture for fuzzy logic control," Int. J. Approx. Reason., vol. 6, pp. 267-292, 1992.
22. G. G. Yen and P. Meesad, "An effective neuro-fuzzy paradigm for machinery condition health monitoring," in Proc. IEEE Int. Joint Conf. Neural Networks, Washington, DC, 1999, pp. 1567-1572.
23. D. E. Gustafson and W. C. Kessel, "Fuzzy clustering with a fuzzy covariance matrix," in Proc. IEEE Control Decision Conf., San Diego, CA, 1979, pp. 761-766.
24. F. Klawon and P. E. Klement, "Mathematical analysis of fuzzy classifiers," Lecture Notes Comput. Sci., vol. 1280, pp. 359-370, 1997.
25. A. Dexter and J. Pakenen, "Demonstrating automated fault detection and diagnosis in real buildings," VTT Building Technology, Espoo, Finland, 2001.
26. R. A. Buswell, "Uncertainty in the first principle model based condition monitoring of HVAC systems," Ph.D. dissertation, Loughborough Univ., Leicestershire, U.K., 2001.
27. K. L. Anderson, G. L. Blackenship, and L. G. Lebow, "A rule-based adaptive PID controller," in Proc 27th IEEE Conf. Decision Control, 1988, pp. 564-569.
28. P. Strobach, Linear Prediction Theory: A Mathematical Basis for Adaptive Systems. New York: Springer-Verlag, 1990.
29. D. P. Filev, T. Larsson, and L. Ma, "Intelligent control for automotive manufacturing-rule based guided adaptation," in IEEE Conf. IECON-2000, Nagoya, Japan, Oct. 2000, pp. 283-288.
30. R. Babuska, H. B. Verbruggen, and H. Hellendoorn, "Promising fuzzy modeling and control methodologies for industrial applications," in Proc. ESIT'99, vol. AB-02, Crete, Greece, June 1999.