Fuzzy modelling via on-line support vector machines

International Journal of Systems Science

Volumn 41, Issue 11, 2010, Pages 1325-1335

  Yu, Wen ^a  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

Author keywords

clustering; fuzzy logic; identification; support vector machines

Indexed keywords

CLUSTERING; FUZZY MODELLING; IDENTIFICATION; LEARNING RATES; MODELLING ERROR; ONLINE SUPPORT; STRUCTURE IDENTIFICATION; SUPPORT VECTOR; TIME VARYING; UPPER BOUND;

FUZZY LOGIC; FUZZY RULES; FUZZY SYSTEMS; MEMBERSHIP FUNCTIONS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 77957803624     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/00207720903045775     Document Type: Article

References (23)

1. Angelov, P. (2004), 'An Approach for Fuzzy Rule-base Adaptation using On-line Clustering', International Journal of Approximate Reasoning, 35, 275-289.
2. Chen, S., and Billings, S.A. (1989), 'Representation of Nonlinear Systems: the NARMAX Model', International Journal of Control, 49, 1012-1032.
3. Chiang, J.-H., and Hao, P.-Y. (2004), 'Suuport Vector Learning Mechanism for Fuzzy Rule-based Modeling: A New Approach', IEEE Transactions on Fuzzy Systems, 12, 1-12.
4. Cristianini, N., and Shawe-Taylor, J. (2000), An Introduction to Support Vector Machines, Cambridge: Cambridge University Press.
5. Chiu, S.L. (1994), 'Fuzzy Model Identification Based on Cluster Estimation', Journal of Intelligent and Fuzzy Systems, 2, 267-278.
6. Engel, Y., Mannor, S., and Meir, R. (2004), 'The Kernel Recursive Least-squares Algorithm', IEEE Transactions on Signal Processing, 52, 2275-2285.
7. Ioannou, P.A., and Sun, J. (1996), Robust Adaptive Control, Upper Saddle River, NJ: Prentice-Hall, Inc.
8. Juang, C.F. (2005), 'Combination of On-line Clustering and Q-value Based GA for Reinforcement Fuzzy System Design', IEEE Transactions on Fuzzy Systems, 13, 289-302.
9. Jang, J.S. (1993), 'ANFIS: Adaptive-network-based Fuzzy Inference System', IEEE Transactions on Systems, Man and Cybernetics, 23, 665-685.
10. Leski, J.M. (2005), 'TSK-fuzzy Modeling Based on -Insensitive Learning', IEEE Transactions on Fuzzy Systems, 13, 181-193.
11. Mandic, D.P., Hanna, A.I., and Razaz, M. (2001), 'A Normalized Gradient Descent Algorithm for Nonlinear Adaptive Filters using a Gradient Adaptive Step Size', IEEE Signal Processing Letters, 8, 295-297.
12. Mamdani, E.H. (1976), 'Application of Fuzzy Algorithms for Control of Simple Dynamic Plant', IEEE Proccedings-Control Theory and Applications, 121, 1585-1588.
13. Mitra, S., and Hayashi, Y. (2000), 'Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework', IEEE Transactions on Neural Networks, 11, 748-769.
14. Mueller, K.-R., Mika, S., Rasch, G., Tsuda, K., and Scholkopf, B. (2001), 'An Introduction to Kernel-Based Learning Algorithm', IEEE Transactions on Neural Networks, 12, 181-201.
15. Oh, S.-K., Pedrycz, W., and Roh, S.-B. (2006), 'Genetically Optimized Fuzzy Polynomial Neural Networks with Fuzzy Set-based Polynomial Neurons', Information Sciences, 176, 3490-3519. (Pubitemid 44310649)
16. Rivals, I., and Personnaz, L. (2003), 'Neural-network Construction and Selection in Nonlinear Modeling', IEEE Transactions on Neural Networks, 14, 804-820.
17. Straszecka, E. (2006), 'Combining Uncertainty and Imprecision in Models of Medical Diagnosis', Information Sciences, 176, 3026-3059.
18. Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., and Vandewalle, J. (2002), Least Square Support Vector Machines, Singapore: World Scientific.
19. Takagi, T., and Sugeno, M. (1985), 'Fuzzy Identification of Systems and its Applications to Medeling and Control', IEEE Transactions on Systems Man and Cybernetics, 1, 116-132.
20. Tzafestas, S.G., and Zikidis, K.C. (2001), 'NeuroFAST: On-line Neuro-fuzzy ART-based Structure and Parameter Learning TSK Model', IEEE Transactions on Systems, Man and Cybernetics, Part B, 31, 797-803.
21. Uncu, O., and Turksen, I.B. (2007), 'A Novel Feature Selection Approach: Combining Feature Wrappers and Filters', Information Sciences, 77, 449-466.
22. Wang, L.X. (1994), Adaptive Fuzzy Systems and Control, Englewood Cliffs, NJ: Prentice-Hall.
23. Yu, W., and Li, X. (2001), 'Some New Results on System Identification with Dynamic Neural Networks', IEEE Transactions on Neural Networks, 12, 412-417. (Pubitemid 32371495)