A fuzzy-neural multi-model for nonlinear systems identification and control

Fuzzy Sets and Systems

Volumn 159, Issue 20, 2008, Pages 2650-2667

  Baruch, Ieroham S ^a   Beltran Lopez, Rafael ^a   Olivares Guzman, Jose Luis ^a   Martin Flores, Jose ^a  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

Author keywords

DC motor control; Direct adaptive control; Fuzzy neural hierarchical multi model; Indirect adaptive control; Recurrent neural networks; Systems identification

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ADAPTIVE SYSTEMS; CHLORINE COMPOUNDS; CONTROL SYSTEMS; CONTROL THEORY; COORDINATION REACTIONS; FUZZY CONTROL; HIERARCHICAL SYSTEMS; IMAGE CLASSIFICATION; INTELLIGENT CONTROL; LAWS AND LEGISLATION; MEMBERSHIP FUNCTIONS; NEURAL NETWORKS; NONLINEAR SYSTEMS; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING;

ADAPTIVE CONTROL; ADAPTIVE TRAJECTORY; DC-MOTOR CONTROL; DIRECT ADAPTIVE CONTROL; FUZZY-NEURAL; FUZZY-NEURAL HIERARCHICAL MULTI-MODEL; FUZZYFICATION; HIERARCHICAL FUZZY; INDIRECT ADAPTIVE CONTROL; INTELLIGENT CONTROL SYSTEMS; LOCAL CONTROL; MULTI MODELING; NEURAL NETWORK MODELLING; NONLINEAR PLANTS; PLANT OUTPUT; RULE-BASED CONTROL; SYSTEMS IDENTIFICATION; TAKAGI-SUGENO;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 49049116696     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2008.01.027     Document Type: Article

References (35)

1. Adamy J., and Kempf R. Regularity and chaos in recurrent fuzzy systems. Fuzzy Sets and Systems 140 2 (2003) 259-284
2. Ahmed M.S. Block partial derivative and its application to neural-net-based direct-model-reference adaptive control. IEE Proc. Control Theory Appl. 141 5 (1994) 305-314
3. Babuska R. Fuzzy Modeling for Control (1998), Kluwer, Norwell, MA, NY
4. I. Baruch, J.M. Flores, R. Garrido, A fuzzy-neural recurrent multi-model for systems identification and control, in: Proc. European Control Conference, ECC'01, Porto, Portugal, September 4-7, 2001, pp. 3540-3545.
5. I. Baruch, J.M. Flores, F. Nava, I.R. Ramirez, B. Nenkova, An advanced neural network topology and learning applied for identification and control of a D.C. motor, in: Proc. First Int. IEEE Symp. on Intelligent Systems, Varna, Bulgaria, September, 2002, pp. 289-295.
6. I. Baruch, J.M. Flores, F. Thomas, R. Garrido, Adaptive neural control of nonlinear systems, in: G. Dorffner, H. Bischof, K. Hornik (Eds.), Proc. Int. Conf. on NNs, ICANN 2001, Vienna, Austria, August 2001, Lecture Notes in Computer Science, Vol. 2130, Springer, Berlin, Heidelberg, NY, 2001, pp. 930-936.
7. I. Baruch, E. Gortcheva, Fuzzy neural model for nonlinear systems identification, in: Proc. AARTC IFAC Workshop, Cancun, Mexico, April 15-17, 1998, pp. 283-288.
8. I. Baruch, L.A. Hernandez, J. Barrera-Cortes, Adaptive discrete-time sliding mode control using recurrent neural networks, in: Proc. Second IFAC Symp. on System, Structure and Control, Oaxaca, Mexico, December 8-10, 2004, file [SSSC112.pdf].
9. Baruch I., Stoyanov I., and Gortcheva E. Topology and learning of a class RNN. ELEKTRIK 4 Suppl. (1996) 35-42
10. I. Baruch, F. Thomas, R. Garrido, E. Gortcheva, A hybrid multi-model neural network for nonlinear systems identification, in: Proc. Int. Joint Conf. on Neural Networks, IJCNN'99, Washington DC, USA, July 10-16, 1999, pp. 4278-4283.
11. Frasconi P., Gori M., and Soda G. Local feedback multilayered networks. Neural Comput. 4 (1992) 120-130
12. Gao Y., and Er M.J. NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets and Systems 150 2 (2005) 331-350
13. Guerra T.M., Kruszewski A., Vermeiren L., and Tirmant H. Conditions of output stabilization for nonlinear models in the Takagi-Sugeno's form. Fuzzy Sets and Systems 157 (2006) 1248-1259
14. Haykin S. Neural Networks, A Comprehensive Foundations. second ed. (1999), Prentice-Hall, Englewood Cliffs, NJ
15. Hunt K.J., Sbarbaro D., Zbikowski R., and Gawthrop P.J. Neural network for control systems (a survey). Automatica 28 (1992) 1083-1112
16. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Systems Man Cybernet. 23 3 (1993) 665-685
17. Kempf R., and Adamy J. Equilibria of recurrent fuzzy systems. Fuzzy Sets and Systems 140 2 (2003) 231-257
18. Lee S.W., and Kim J.H. Robust adaptive stick-slip friction compensation. IEEE Trans. Indust. Electron. 42 5 (1995) 474-479
19. Lin C.J., and Chen C.H. Identification and prediction using recurrent compensatory neuro-fuzzy systems. Fuzzy Sets and Systems 150 2 (2005) 307-330
20. Lin C.J., and Chen C.H. A compensation-based recurrent fuzzy neural network for dynamic system identification. European J Oper. Res. 172 2 (2006) 696-715
21. Lin C.T., and Lee C.S.G. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (1996), Prentice-Hall, Englewood Cliffs, NJ
22. Mastorocostas P.A., and Theocharis J.B. A recurrent fuzzy-neural model for dynamic system identification. IEEE Trans. Systems Man Cybernet. Part B: Cybernet. 32 (2002) 176-190
23. Mastorocostas P.A., and Theocharis J.B. An orthogonal least-squares method for recurrent fuzzy-neural modeling. Fuzzy Sets and Systems 140 2 (2003) 285-300
24. Narendra K.S., and Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1 1 (1990) 4-27
25. Nürnberger A. A hierarchical recurrent neuro-fuzzy model for system identification. Internat. J. Approx. Reason. 32 2-3 (2003) 153-170
26. Nürnberger A., Radetzky A., and Kruse R. Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems. Neurocomputing 36 1-4 (2001) 123-147
27. Omatu S., Khalil M., and Yusof R. Neuro-Control and its Applications (1995), Springer, London
28. D.T. Pham, S. Yildirim, Robot control using Jordan neural networks, in: Proc. Internat. Conf. on Recent Advances in Mechatronics, Istanbul, Turkey, August 14-16, II, 1995, pp. 888-893.
29. Sastry P.S., Santharam G., and Unnikrishnan K.P. Memory networks for identification and control of dynamical systems. IEEE Trans. Neural Networks 5 (1994) 306-320
30. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Systems Man Cybernet. 15 (1985) 116-132
31. Teixeira M., and Zak S. Stabilizing controller design for uncertain nonlinear systems using fuzzy models. IEEE Trans. Systems Man Cybernet. 7 (1999) 133-142
32. Theocharis J.B. A high-order recurrent neuro-fuzzy system with internal dynamics: application to the adaptive noise cancellation. Fuzzy Sets and Systems 157 4 (2006) 471-500
33. Wan E., and Beaufays F. Diagrammatic method for deriving and relating temporal neural networks algorithms. Neural Comput. 8 (1996) 182-201
34. Yip P.P.C., and Pao Y.H. A recurrent neural net approach to one-step-ahead control problems. IEEE Trans. Systems Man Cybernet. 24 (1994) 678-683
35. Zhou S.M., and Xu L.D. A new type of recurrent fuzzy-neural network for modeling dynamic systems. Knowledge Based Systems 14 5-6 (2001) 243-251