An approach for fuzzy rule-base adaptation using on-line clustering

International Journal of Approximate Reasoning

Volumn 35, Issue 3, 2004, Pages 275-289

  Angelov, Plamen ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Fuzzy rule based models identification; On line clustering; Parameter estimation; Takagi Sugeno fuzzy models

Indexed keywords

ADAPTIVE SYSTEMS; ALGORITHMS; COMPUTATIONAL METHODS; FERMENTATION; KNOWLEDGE BASED SYSTEMS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; OXIDATION; PARAMETER ESTIMATION; SUGARS;

FUZZY RULE-BASED MODELS IDENTIFICATION; ON-LINE CLUSTERING; TAKAGI-SUGENO FUZZY MODELS;

FUZZY SETS;

EID: 1142279663     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2003.08.006     Document Type: Conference Paper

References (19)

1. Lin W.S., Tsai C.-H. Self-organizing fuzzy control of multi-variable systems using learning vector quantization network. Fuzzy Sets and Systems. 124:2001;197-212.
2. Lin F.-J., Lin C.-H., Shen P.-H. Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE Transactions on Fuzzy Systems. 9(5):2001;751-759.
3. Specht D.F. A general regression neural network. IEEE Transactions on NN. 2(6):1991;568-576.
4. Stergioulas L.K., Vassiliadis V.S., Vourdas A. Construction of quantum states from an optimally truncated von Neumann lattice of coherent states. Journal of Physics A: Mathematical and General. 32:1999;3169-3178.
5. Angelov P.P. Evolving rule-based models: a tool for design of flexible adaptive systems. 2002;Springer, Physica-Verlag, Heidelberg.
6. Angelov P.P., Buswell R.A. Identification of evolving fuzzy rule-based models. IEEE Transactions on Fuzzy Systems. 5(10):2002;667-677.
7. P.P. Angelov et al., Evolving rule-based control, in: EUNITE Symposium, Tenerife, Spain, 2001, pp. 36-41.
8. Angelov P.P., Filev D.P. An approach to on-line identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems Man and Cybernetics Part B. 33(13):2003.
9. Chiu S.L. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems. 2:1994;267-278.
10. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on SMC. 15:1985;116-132.
11. Astroem K.J., Wittenmark B. Adaptive Control. 1989;Addison Wesley, MA, USA.
12. Kasabov N.K., Song Q. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Transactions on Fuzzy Systems. 10(2):2002;144-154.
13. Sugeno M., Yasukawa T. A fuzzy logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems. 1(1):1993;7-31.
14. Johanson T.A., Murray-Smith R. Operating regime approach to non-linear modeling and control. Murray-Smith R., Johanson T.A. Multiple Model Approaches to Modeling and Control. 1997;3-72 Taylor Francis, Hants, UK.
15. Angelov P., Buswell R. Automatic generation of fuzzy rule-based models from data by genetic algorithms. Information Sciences. 150(1/2):2003;17-31.
16. Lim M.H., Rahardja S., Gwee B.H. A GA paradigm for learning fuzzy rules. Fuzzy Sets and Systems. 82:1996;177-186.
17. Gabor D., Wildes W., Woodcock R. A universal nonlinear filter, predictor and simulator which optimizes itself by a learning process. Proceedings of the IEE. 108B:1961;422-438.
18. Ivakhnenko A.G. Polynomial theory of complex systems. IEEE Transactions on Systems Man and Cybernetics. 1(4):1971;364-378.
19. Jang J.S.R. ANFIS: adaptive network-based fuzzy inference systems. IEEE Transactions on Systems Man and Cybernetics. 23(3):1993;665-685.