A dynamically generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles

Engineering Applications of Artificial Intelligence

Volumn 15, Issue 6, 2002, Pages 541-550

  Wang, Lipo ^a   Frayman, Yakov ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Dynamically generated fuzzy neural network; Neurofuzzy control; Tandem cold rolling mill

Indexed keywords

ALGORITHMS; COLD ROLLING MILLS; DATA ACQUISITION; NEURAL NETWORKS; THREE TERM CONTROL SYSTEMS; VIBRATION CONTROL;

TANDEM COLD ROLLING;

FUZZY SETS;

EID: 0038560988     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0952-1976(03)00006-X     Document Type: Article

References (48)

1. Ash T. Dynamic node creation in backpropagation networks. Connection Science. 1(4):1989;365-375.
2. Åström K., Wittenmark B. Computer Controlled Systems, Theory and Design. 2nd Edition:1990;Prentice-Hall, Englewood Cliffs, NJ.
3. Berenji H., Khedkar P. Learning and tuning fuzzy logic controllers through reinforcements. IEEE Transactions on Neural Networks. 3(5):1993;724-740.
4. Brown M., Harris C. Neurofuzzy Adaptive Modelling and Control. 1994;Prentice-Hall, Englewood Cliffs, NJ.
5. Bryant G. Automation of Tandem Mills. 1973;The Iron and Steel Institute, London.
6. Cai L.Y., Kwan H.K. Fuzzy classifications using fuzzy inference networks. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics. 28(3):1998;334-347.
7. Cybenko G. Approximations by superposition of a sigmoidal function. Mathematics of Control, Signals, and Systems. 2:1989;303-314.
8. Driankov D., Hellendoorn H., Palm R. Some research directions in fuzzy control. Nguyen H., Sugeno M., Tong R., Yager R. Theoretical Aspects of Fuzzy Control. 1995;281-312 Wiley, New York.
9. Dubois D., Prade H. A unifying view of comparison indices in a fuzzy set theoretic framework. Yager R.R. Fuzzy Sets and Possibility Theory: Recent Developments. 1982;Pergamon, New York.
10. Elman J. Finding structure in time. Cognitive Science. 14(2):1990;179-211.
11. Fahlman S. Faster-learning variations on back-propagation: an empirical study. Sejnowski T., Hinton G., Touretzky D. 1988 Connectionist Models Summer School. 1988;38-51 Morgan Kaufmann, San Mateo, CA.
12. Fahlman, S., 1991. The recurrent cascade-correlation learning architecture. Technical Report CMU-CS-91-100, School of Computer Science, Carnegie Mellon University.
13. Fahlman, S., Lebiere, C., 1990. The cascade-correlation learning architecture. In: Touretzky, D.S. (Ed.), Advances in Neural Information Processing Systems, Vol. 2. Morgan Kaufmann, San Mateo, CA, pp. 524-532.
14. Frean M. The upstart algorithm. a method for constructing and training feedforward neural networks Neural Computation. 1:1990;198-209.
15. Funahashi K., Nakamura Y. Approximation of dynamical systems by continuous time recurrent neural networks. Neural Networks. 6(6):1993;801-806.
16. Giles C.L., Chen D., Sun G.Z., Chen H.H., Lee Y.C., Goudreau M.W. Constructive learning of recurrent neural networks. limitations of recurrent cascade correlation and a simple solution IEEE Transactions on Neural Networks. 6:1995;829-836.
17. Harakawa, T., Yui, K., Sumitani, E., 1987. Development of spindle torsional vibration control system using observer for tandem cold mill in steel production process. Automatic Control - Proceedings of the 10th Triennial World Congress of the IFAC, Vol. 3. pp. 109-113.
18. Harris C., Moore C., Brown M. Intelligent Control: Aspects of Fuzzy Logic and Neural Nets. 1993;World Scientific, Singapore.
19. Higgins C.M., Goodman R.M. Fuzzy rule-based networks for control. IEEE Transactions on Fuzzy Systems. 2(1):1994;82-88.
20. Horikawa S., Furahashi T., Uchikawa Y. On fuzzy modelling using neural networks with the backpropagation algorithm. IEEE Transactions on Neural Networks. 3:1992;801-806.
21. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks are universal approximators. Neural Networks. 2(5):1989;359-366.
22. Hunt K., Sbarbaro D., Zbikowski R., Gawthrop P. Neural networks for control systems - a survey. Automatica. 28(6):1992;1083-1112.
23. Ishibuchi H., Tanaka H., Okada H. Interpolation of fuzzy if-then rules by neural networks. International Journal of Approximate Reasoning. 10:1994;3-27.
24. Jang J.-S.R. ANFIS. adaptive-network-based fuzzy inference systems IEEE Transactions on Systems, Man, and Cybernetics. 23(3):1993;665-685.
25. Jin L., Nikiforuk P., Gupta M. Fast neural learning and control of discrete-time nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics. 25(3):1995;479-488.
26. Juang C., Lin C. A recurrent self-organizing neural fuzzy inference network. IEEE Transactions on Neural Networks. 10(4):1999;828-845.
27. Kawaguchi T., Ueyama T. Steel Industry. Control System. Vol. 2:1989;Gordon and Breach Science Pub. New York, NY.
28. Kosko B. Neural Networks and Fuzzy Systems: A Dynamical Approach to Machine Intelligence. 1991;Prentice-Hall, Englewood Cliffs, NJ.
29. Kuo B. Automatic Control Systems. 6th Edition:1991;Prentice-Hall, Englewood Cliffs, NJ.
30. Lin C.-T., Lee C.S.G. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. IEEE Transactions on Fuzzy Systems. 2(1):1994;46-63.
31. Mezard M., Nadal J. Learning in feedforward layered networks. the tiling algorithms Journal of Physics A. 22:1989;2191-2203.
32. Nadal J. Study of a growth algorithm for feedforward neural network. International Journal of Neural Systems. 1:1989;55-59.
33. Narendra K., Mukhopadhyay S. Adaptive control of nonlinear multivariable systems using neural networks. Neural Networks. 7(5):1994;737-752.
34. Narendra K., Pathasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks. 1(1):1990;4-27.
35. Nauck, D., Kruse, R., 1993. A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation. In: Proceedings of the IEEE International Conference on Neural Networks ICNN'93, San Francisco, CA, pp. 1022-1027.
36. Neumerkel, D., Franz, J., Krüger, L., Hidiroglu, A., 1994. Real-time application of neural model predictive control for an induction servo drive. Proceedings of the Third IEEE Conference on Control Applications, Vol. 1, Glasgow, UK, pp. 433-438.
37. Nie J., Linkens D. Learning control using fuzzified self-organizing radial basis function network. IEEE Transactions on Fuzzy Systems. 1(4):1993;280-287.
38. Nie J., Linkens D. Fuzzy-Neural Control: Principles, Algorithms and Applications. 1995;Prentice-Hall, Englewood Cliffs, NJ.
39. Ogata K. Discrete-Time Control Systems. 1987;Prentice-Hall, Englewood Cliffs, NJ.
40. Sbarbaro-Hofer D., Neumerkel D., Hunt K. Neural control of a steel rolling mill. IEEE Control Systems Journal. 13(3):1993;69-75.
41. SNNS (Stuttgart Neural Network Simulator) User Manual, 1995. Version 4.1, University of Stuttgart, Institute for Parallel and Distributed High Performance Systems (IPVR), 6/95.
42. Sugeno M., Tanaka K. Successive identification of fuzzy model and its application to prediction of complex system. Fuzzy Sets and Systems. 42:1991;315-344.
43. Takagi T., Sugeno M. Fuzzy identification of systems and its applications to modelling and control. IEEE Transactions on Systems, Man and Cybernetics. 15(1):1985;116-132.
44. Wang L.-X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis. 1994;PTR Prentice-Hall, Englewood Cliffs, NJ.
45. Wang L., Langari R. Building Sugeno-type models using fuzzy discretization and orthogonal parameter estimation techniques. IEEE Transactions on Fuzzy Systems. 3:1995;454-458.
46. Wang L.-X., Mendel J. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Transactions on Neural Networks. 3(5):1992;807-814.
47. Yen, J., Langari, R., Zadeh, L. (Eds.), 1995. Industrial Applications of Fuzzy Logic and Intelligent Systems. IEEE Press, New York.
48. Zipser, D., 1990. Subgrouping reduces complexity and speeds up learning in recurrent networks. In: Touretzky, D.S. (Ed.), Advances in Neural Information Processing Systems, Vol. 2. Morgan Kaufmann, San Mateo, CA, pp. 638-641.