A new fuzzy neural network with fast learning algorithm and guaranteed stability for manufacturing process control

Fuzzy Sets and Systems

Volumn 132, Issue 2, 2002, Pages 201-216

  Zhou, Yunfei ^a   Li, Shuijin ^a   Jin, Rencheng ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Learning; Lyapunov's direct method; Neuro fuzzy systems; Stability analysis

Indexed keywords

ALGORITHMS; LYAPUNOV METHODS; MEMBERSHIP FUNCTIONS; NEURAL NETWORKS; PROCESS CONTROL;

STABILITY ANALYSIS;

FUZZY SETS;

EID: 0036888736     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(01)00234-2     Document Type: Article

References (41)

1. Asakawa K., Takagi H. Neural networks in Japan. Commun. ACM. 37:1994;106-112.
2. Bernard J.F. Use of rule-based system for process control. IEEE Contr. System Mag. 8:1988;3-13.
3. Brown M., Harris C. Neurofuzzy Adaptive Modelling and Control. 1994;Prentice-Hall, Englewood Cliffs, NJ.
4. Chao C.T., Teng C.C. Implementation of a fuzzy inference system using a normalized fuzzy neural network. Fuzzy Sets and Systems. 75:1995;17-31.
5. Chai J.S., Tan S.H., Chen Q. A general fuzzy control scheme for nonlinear processes with stability analysis. Fuzzy Sets and Systems. 100:1998;179-195.
6. Cheng C.M., Rees N.W. Stability analysis of fuzzy multivariable systems: vector Lyapunov function approach. IEE Proc. Control Theory Appl. 144:1997;403-412.
7. Chen Y.C., Teng C.C. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems. 73:1995;291-312.
8. Du R.X., Elbestrawi M.A., Li S. Tool condition monitoring in turning using fuzzy set theory. Int. J. Mach. Tool Manufact. 32:1992;781-796.
9. Feng G., Cao S.G., Chak C.K. Design of fuzzy control systems with guaranteed stability. Fuzzy Sets and Sytems. 85:1997;1-10.
10. Hasegawa T., Horikawa S., Furuhashi T. On design of adaptive fuzzy controller using fuzzy neural networks and a description of its dynamical behavior. Fuzzy Sets and Systems. 71:1995;3-23.
11. Horikawa S.J., Furuhashi T., Uchikawa Y. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithms. IEEE Trans. Neural Networks. 3:1992;801-806.
12. Hwang G.C., Lin S.C. A stability approach to fuzzy control design for nonlinear systems. Fuzzy Sets and Systems. 48:1992;279-287.
13. Jang J.S. Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. Neural Networks. 3:1992;723-741.
14. Karayiannis N.B., Venetsanopoulos A.N. Artificial Neural Networks, Learning Algorithm, Performance Evaluation and Applications. 1993;Kluwer Academic Publishers, Dordrecht.
15. Kim W.C., Ahn S.C., Kwon W.H. Stability analysis and stabilization of fuzzy state space models. Fuzzy Sets and Systems. 71:1995;131-142.
16. Kiszka J.B., Gupta M.M., Nikiforuk P.N. Energetistic stability of fuzzy dynamic systems. IEEE Trans. Systems, Man Cybern. SMC-15:1985;783-791.
17. Krishnapuram R., Lee J. Fuzzy-set-based hierarchical networks for information fusion in computer vision. Neural Networks. 5:1992;335-350.
18. Ku C.C., Lee K.Y. Diagonal recurrent neural networks for dynamic systems control. IEEE Trans. Neural Network. 6:1995;144-155.
19. Kumar S.R., Majumder D.D. Application of FCSs to industrial processes. Automatica. 13:1997;235-242.
20. R.J. Kuo, Multi-sensor integration for intelligent control of machining through artificial neural networks and fuzzy modeling, Ph.D. Dissertation, The Pennsylvania State University, 1994.
21. Kuo R.J., Cohen P.H. Manufacturing process control through integration of neural networks and fuzzy model. Fuzzy Sets and Systems. 98:1998;15-31.
22. Lee K.M., Kwak D.H., Leekwang H. Tuning of fuzzy models by fuzzy neural networks. Fuzzy Sets and Systems. 76:1995;47-61.
23. Lenone R.D., Capparuccia R., Merelli E. A successive overrelaxation backpropagation algorithm for neural-network training. IEEE Trans. Neural Network. 9:1998;381-388.
24. Lin C.T., Lin C.J., Lee C.S.G. Fuzzy adaptive learning control network with on-line neural learning. Fuzzy Sets and Systems. 71:1995;25-45.
25. Luo Z.A., Tseng P. Analysis of an approximate gradient projection method with application to the backpropagation algorithm. Optim. Methods Software. 4:1994;85-102.
26. L.J. Maguire, T.M. MeGinnity, L.J. McDaid, A fuzzy neural network for approximate fuzzy reasoning, in: Hybrid Intelligent Systems: Fuzzy Logic, Neural Networks and Genetic Algorithm Da Rnan (Ed.), Kluwer Academic Publisher, Dordrecht, 1997, pp. 35-58 (Chapters 8 and 9).
27. McLoone S., Brown M.D., Irwin G. A hybrid linear/nonlinear training algorithm for feedforward neural networks. IEEE Trans. Neural Network. 9:1998;669-684.
28. Michels K. Numerical stability analysis for a fuzzy or neural network controller. Fuzzy Sets and Systems. 89:1997;335-350.
29. Chen Minyou, Linkens D.A. A hybrid neuro-fuzzy PID controller. Fuzzy Sets and Systems. 99:1998;27-36.
30. Polycarpou M.M., Ioannou P.A. Learning and convergence analysis of neural-type structured networks. IEEE Trans. on Neural Networks. 3:1992;39-50.
31. Shann J.J., Fu H.C. A fuzzy neural network for rule acquiring on fuzzy control systems. Fuzzy Sets and Systems. 71:1995;345-357.
32. Tanaka K., Sugeno M. Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems. 45:1992;135-156.
33. Tarng Y.S., Hwang S.T. A neural network controller for constant turning force. Int. J. Mach. Tools Manufact. 4:1994;453-460.
34. Tarng Y.S., Cheng S.T. Fuzzy control of feed rate in end milling operations. Int. J. Mach. Tools Manufact. 4:1993;643-650.
35. Wang L.X. Adaptive Fuzzy Systems and Control. 1994;Prentice-Hall, Englewood Cliffs, NJ.
36. Wang L.X. Combining mathematical model and heuristics into controllers: an adaptive fuzzy control approach. Fuzzy Sets and Systems. 89:1997;151-156.
37. L.X. Wang, J.M. Mendel, Back-propagation fuzzy system as nonlinear dynamic system identifiers, IEEE International Conference on Fuzzy system, 1992, pp. 1409-1418.
38. S. Yamazkai, S. Miyamoto, H. Ihara, Fuzzy control for automatic train operation system, Proceedings of the Fourth IFAC/IFIP/IFORS International Congress on control in Transportation Systems, Baden, 1983.
39. Yasunobu S., Miyamoto S. Automatic train operation by predictive fuzzy control. Ind. Appl. Fuzzy Control. 1985;835-838.
40. Ying H. Practical design of nonlinear fuzzy controllers with stability analysis for regulating processes with unknown mathematical models. Automatica. 30:1994;1185-1195.
41. Zhang Y.Q., Kandel A. Compensatory neurofuzzy systems with fast learning algorithms. IEEE Trans. Neural Networks. 9:1998;83-105.