Support vector machines based neuro-fuzzy control of nonlinear systems

Neurocomputing

Volumn 73, Issue 10-12, 2010, Pages 2097-2107

  Iplikci, S ^a  

^a PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

Intelligent control; Neuro fuzzy systems; Support vector machines

Indexed keywords

ADAPTIVE NETWORK FUZZY INFERENCE SYSTEMS; GENERALIZATION ABILITY; GLOBAL MINIMA; GRADIENT INFORMATIONS; NEURO-FUZZY SYSTEMS; NEUROFUZZY CONTROL; NEUROFUZZY SYSTEM; NONLINEAR PLANT; PLANT DYNAMICS; SIMULATION RESULT; STEADY STATE TRACKING; SVM ALGORITHM; SVM MODEL;

ADAPTIVE CONTROL SYSTEMS; CONTROLLERS; FUZZY CONTROL; FUZZY INFERENCE; FUZZY SYSTEMS; INTELLIGENT CONTROL; VECTORS;

SUPPORT VECTOR MACHINES;

ARTICLE; CLASSIFICATION ALGORITHM; COMPUTER MODEL; COMPUTER SIMULATION; FUZZY SYSTEM; MATHEMATICAL COMPUTING; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; SYSTEM ANALYSIS;

EID: 77952551246     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.02.008     Document Type: Article

References (29)

1. Feng G. A survey on analysis and design of model-based fuzzy control systems. IEEE Transactions on Fuzzy Systems 2006, 14(5):676-697.
2. Barazane L., Khwaldeh A., Jumah M., El-Qawasmeh E. Neuro-fuzzy sliding mode control applied to the field-oriented control of an induction motor. Neural Network World 2007, 17(4):335-349.
3. Wang L.X. Stable adaptive fuzzy control of nonlinear systems. IEEE Transactions on Fuzzy Systems 1993, 1(2):146-155.
4. Zhang J. A nonlinear gain scheduling control strategy based on neuro-fuzzy networks. Industrial and Engineering Chemistry Research 2001, 40(14):3164-3170.
5. Baojiang Z., Shiyong L. Ant colony optimization algorithm and its application to neuro-fuzzy controller design. Journal of Systems Engineering and Electronics 2007, 18(3):603-610.
6. Seng T.L., Khalid M.B., Yusof R. Tuning of a neuro-fuzzy controller by genetic algorithm. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics 1999, 29(2):226-236.
7. Jang J.S.R., Sun C.T., Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence 1997, Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
8. Jang J.S.R. Self-learning fuzzy controllers based on temporal backpropagation. IEEE Transactions on Neural Networks 1992, 3(5):714-721.
9. Psaltis D., Sideris A., Yamamura A.A. A multilayered neural network controller. IEEE Control Systems Magazine 1988, 8:17-21.
10. Jang J.S.R., Sun C.T. Neuro-fuzzy modeling and control. Proceedings of the IEEE 1995, 83(3):378-406.
11. M.A. Denai, F. Palis, A. Zeghbib, Anfis based modelling and control of non-linear systems: a tutorial, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2004, pp. 3433-3438.
12. Y. Chen, Hybrid soft computing approach to identification and control of nonlinear systems, Ph.D. Thesis, Kumamoto University, Japan, 2001.
13. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer-Verlag, London.
14. Vapnik V.N. Statistical Learning Theory 1998, Wiley-Interscience, New York.
15. Cristianini N., Taylor J.S. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, New York.
16. Scholkopf B., Burges C.J.C., Smola A.J. Advances in Kernel Methods: Support Vector Learning 1999, The MIT Press, Cambridge, MA, USA.
17. Iplikci S. Support vector machines-based generalized predictive control. International Journal of Robust and Nonlinear Control 2006, 16(17):843-862.
18. Iplikci S. Online trained support vector machines-based generalized predictive control of non-linear systems. International Journal of Adaptive Control and Signal Processing 2006, 20(10):599-621.
19. H. Liu, H. Wu, F. Qian, Double inverted pendulum control based on support vector machines and fuzzy inference, in: Advances in Neural Networks-ISNN 2006, PT 2, Proceedings, vol. 3972, 2006, pp. 1124-1130.
20. Xi X.C., Poo A.N., Chou S.K. Support vector regression model predictive control on a hvac plant. Control Engineering Practice 2007, 15(8):897-908.
21. Wang H., Pi D., Sun Y. Online svm regression algorithm-based adaptive inverse control. Neurocomputing 2007, 70(4-6):952-959.
22. Jang J.S.R. Anfis: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 1993, 23(3):665-684.
23. Brogan W.L. Modern Control Theory 1991, Prentice-Hall, Inc, Upper Saddle River, NJ, USA. third ed.
24. A.J. Smola, B. Scholkopf, A tutorial on support vector regression, NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, 1998.
25. Nocedal J., Wright S.J. Numerical Optimization 1999, Springer, New York.
26. Venkataraman P. Applied Optimization with MATLAB Programming 2002, Wiley-Interscience, New York.
27. Sindelar R., Babuska R. Input selection for nonlinear regression models. IEEE Transactions on Fuzzy Systems 2004, 12(5):688-696.
28. Ungar L.H. A Bioreactor Benchmark for Adaptive Network-based Process Control 1990, MIT Press, Cambridge, MA, USA.
29. Wu W., Chou Y.S. Adaptive feedforward and feedback control of nonlinear time-varying uncertain systems. International Journal of Control 1999, 72(12):1127-1138.