Recursive learning algorithms for training fuzzy recurrent models

International Journal of Intelligent Systems

Volumn 11, Issue 12, 1996, Pages 1059-1098

  Theocharis, John ^a   Vachtsevanos, George ^b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; COMPUTATIONAL METHODS; DIGITAL FILTERS; FEEDBACK; FUZZY SETS; INFERENCE ENGINES; LEARNING ALGORITHMS; MATHEMATICAL MODELS; PERFORMANCE; POLYNOMIALS; RECURSIVE FUNCTIONS;

DYNAMICAL ADAPTIVE FUZZY NEURAL NETWORK; EPOCHWISE; RECURSIVE TRAINING ALGORITHM;

NEURAL NETWORKS;

EID: 0030389065     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/(sici)1098-111x(199612)11:12<1059::aid-int3>3.0.co;2-m     Document Type: Article

References (26)

1. K.S. Naredra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. on Neural Networks, 1, 4-27 (1990).
2. Nguyen and B. Widrow, "Neural networks for self-learning control systems," IEEE Control Syst. Mag., April 1990.
3. K.M. Hornik, Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, 2, 359-366 (1989).
4. D. Rumelhart, G. Hinton, and R. Williams, Parallel Distributed Processing, MIT Press, Cambridge, MA, 1986.
5. R.J. Williams and D. Zipser, "A learning algorithm for continually running fully recurrent neural networks," Neural Computation, 270-277 (1989).
6. S.Z. Quin, H.T. Su, and T.J. McAvoy, "Comparison of four neural net learning methods for dynamic system identification," IEEE Trans. Neural Networks, 3(1), 122-130 (1992).
7. B. Srinivasan, U.R. Prasad, and N.J. Rao, "Back-propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural models," IEEE Trans. Neural Nets, 5(2), 213-228 (March 1994).
8. K.S. Naredra and K. Parthasarathy, "Back propagation in dynamical systems containing neural networks," Center for Syst. Sci. of Electrical Eng., Yale University, New Haven, CT, Tech. Rep. 8905, March 1989.
9. K.S. Naredra and K. Parthasarathy, "Gradient methods for the optimization of dynamic systems containing neural networks," IEEE Trans. Neural Networks, 252-262, (1991).
10. P. Werbos, "Generalization of back-propagation with application to recurrent gas market model," Neural Networks, 339-356 (1988).
11. S.W. Piche, "Steepest descent algorithms for neural network controllers and filters," IEEE Trans. Neural Networks, 5(2). 198-212 (March 1984).
12. M. Sugeno and G.T. Kang, "Structure identification of fuzzy model," Fuzzy Sets Syst. 28, 15-33 (1988).
13. H. Nomura, I. Hayashi, and N. Wakami, "A learning method of fuzzy inference rules by descent method," Proc. IEEE Conf. on Fuzzy Systems, 1992, pp. 203-210.
14. L.X. Wang and J.M. Mendel, "Back-propagation fuzzy system as nonlinear dynamic system identifiers," Proc. IEEE Conf. on Fuzzy Systems, 1992, pp. 1409-1418.
15. L.X. Wang and J.M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Trans. Syst. Man Cybern., 22(6), 1414-1427 (1992).
16. J.S. Jang, "Self learning fuzzy controllers based on temporal back propagation," IEEE Trans. on Neural Networks, 3(5), 714-723 (1992).
17. C.T. Lin and C.S.G. Lee, "Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems," IEEE Trans. on Fuzzy Systems, 2(1), 46-63 (Feb. 1994).
18. T. Takagi and M. Sugeno, "Derivation of fuzzy control rules from human operator's control actions," in Proc. IFAC Symp. Fuzzy Information, Knowledge Representation and Decision Analysis, July 1983, pp. 55-60.
19. H. Bersini and V. Giorrini, "Recurrent fuzzy systems," In Proc. FUZZ-IEEE'94, 1, pp. 193-198. Orlando, June 26-29, 1994.
20. M. Sugeno and T. Yasukawa, "A fuzzy logic based approach to qualitative modeling," IEEE Trans. Fuzzy Syst., 1(1), 7-31 (Feb. 1993).
21. K. Tanaka, M. Sano, and H. Watanabe, "Modeling and control of carbon monoxide concentration using a neuro-uzzy technique," IEEE Trans. Fuzzy Syst. 3(3), 271-279 (Aug. 1995).
22. Y. Lin and G. Cunningham, "A new approach to fuzzy-neural system modeling," IEEE Trans. Fuzzy Syst., 3(2), 190-198 (1995).
23. C. Sun and J. Jang, "Fuzzy modeling based on generalized neural networks and fuzzy clustering objective functions," Proc. on the 30th Conf. on Decision and Control, Brighton, 1991, pp. 2924-2929.
24. R.J. Williams and J. Peng, "An efficient gradient-based algorithm for on-line training of recurrent network trajectories," Neural Computation, 490-501 (1990).
25. P. Werbos, "Beyond regression: New rules for prediction and analysis in the behavioral sciences," Ph.D. Thesis, Harvard University, Cambridge, MA, August 1974.
26. B. Windrow and S.D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ. Prentice-Hall, 1985.