On-line design of takagi-sugeno models

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2715, Issue , 2003, Pages 576-584

  Angelov, Plamen ^a   Filev, Dimitar ^b  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b FORD MOTOR COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATA REDUCTION; FUZZY CONTROL; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; ROBOTICS; FAULT DETECTION; FUZZY INFERENCE; FUZZY SYSTEMS; LINEAR CONTROL SYSTEMS; MODAL ANALYSIS;

FAULT DETECTION; FUZZY MODELS; KNOWLEDGE EXTRACTION; MODEL STRUCTURE;

ONLINE SYSTEMS; STRUCTURAL DESIGN;

EVOLVING TAKAGI-SUGENO; KLUYVEROMYCES LACTIS; KNOWLEDGE EXTRACTION; NON LINEAR CONTROL; SPATIAL PROXIMITY; SUPERVISED AND UNSUPERVISED LEARNING; TAKAGI-SUGENO MODELS; TIME SERIES FORECASTING;

EID: 7044264242     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-44967-1_69     Document Type: Conference Paper

References (16)

1. Yager, R., Filev, D. : Essentials of Fuzzy Modeling and Control. NY: John Wiley, (1994).
2. Johanson, T. A., Murray-Smith, R.: Operating regime approach to non-linear modeling and control. In: Multiple Model Approaches to Modeling and Control. Murray-Smith, R., Johanson T. A. (eds.). Hants, UK: Taylor Francis (1992) 3-72
3. Jang, J. S. R.: ANFIS: Adaptive network-based fuzzy inference systems, IEEE Transactions on Systems, Man & Cybernetics. 23 (3) (1993) 665-685
4. Angelov, P.: Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg, Germany: Springer-Verlag (2002).
5. Takagi, T., Sugeno, M. : Fuzzy identification of systems and its application to modeling and control. IEEE Trans. on Systems, Man and Cybernetics. 15 (1985) 116-132
6. Filev, D. P.: Rule-base guided adaptation for mode detection in process control. In: 9th IFSA World Congress, Vancouver, BC, Canada (2001) 1068-1073
7. Chiu, S. L.: Fuzzy model identification based on cluster estimation, J. of Intell. & Fuzzy Syst. 2 (1994) 267-278
8. Yen, J., Wang, L., Gillespie, C. W.: Improving the Interpretability of TSK Fuzzy Models by Combining Global and Local Learning. IEEE Trans. on Fuzzy Syst. 6 (1998) 530-537
9. Lin, F.-J., Lin, C.-H., Shen, P.-H.: Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE Tr. Fuzzy Syst. 9 (2001) 751-759
10. Astroem, K. J., Wittenmark, B.: Adaptive Control. Addison Wesley, MA, USA, 1989
11. Kasabov, N. K., Song, Q.: DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its application for time-series prediction. IEEE Tr. on Fuzzy Syst. 10 (2002) 144-154
12. Vapnik V., The Nature of Statistical Learning Theory. New York: Springer-Verlag (1995).
13. Angelov, P. , D. Filev, "An approach to on-line identification of Takagi-Sugeno fuzzy models. IEEE Trans. on Systems, Man and Cybernetics-B, to appear.
14. Bezdek, J., Cluster Validity with Fuzzy Sets : J of Cybernetics 3(3) (1974) 58-71
15. Gustafson, D. E., Kessel, W. C.: Fuzzy clustering with a fuzzy covariance matrix. IEEE Control and Decision Conference. San Diego, CA, USA (1979) 761-766
16. Angelov, P., Simova, E., Beshkova, D.: Control of cell protein synthesis from Kluyweromyces marxianus var. lactis MC5. Biotech. & Bio Eq. 10(1996) 44-50