A new recurrent neurofuzzy network for identification of dynamic systems

Fuzzy Sets and Systems

Volumn 158, Issue 10, 2007, Pages 1023-1035

  Gonzalez Olvera, Marcos Angel ^a   Tang, Yu ^a  

^a UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO   (Mexico)

Author keywords

Neurofuzzy systems; Recurrent networks; Stability; System identification

Indexed keywords

COMPUTATIONAL METHODS; FEATURE EXTRACTION; IDENTIFICATION (CONTROL SYSTEMS); LINEAR SYSTEMS; NONLINEAR SYSTEMS;

NEUROFUZZY SYSTEMS; REAL-TIME RECURRENT LEARNING ALGORITHM (RTRLA); RECURRENT NETWORKS;

FUZZY NEURAL NETWORKS;

EID: 33947209499     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2006.10.002     Document Type: Article

References (27)

1. Barabanov N.E., and Prokhorov D.V. Stability analysis of discrete-time recurrent neural networks. IEEE Trans. on Neural Networks 13 2 (2002) 292-303
2. S.L. Chiu, A cluster estimation method with extension to fuzzy model identification, Proc. of FUZZ-IEEE'94, 1994, pp. 1240-1245.
3. Chou J.-H., and Chen S.-H. Stability analysis of the discrete takagi-sugeno fuzzy model with time-varying consequent uncertainties. Fuzzy Sets and Systems 118 2 (2001) 271-279
4. Elman J.L. Finding structure in time. Cogn. Sci. 14 (1990) 179-211
5. V. Gorrini, H. Bersini, Recurrent fuzzy systems, Proc. Third Conf. on Fuzzy Systems (FUZZ-IEEE 94), Orlando.
6. Horn R., and Johnson C. Matrix Analysis (1985), Cambridge University Press, Cambridge
7. Jang J.-S.R. Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Systems Man and Cybernet. 23 (1993) 665-685
8. Jin L., and Gupta M.M. Stable dynamic backpropagation learning in recurrent neural networks. IEEE Trans. Neural Networks 10 6 (1999) 1321-1334
9. Juang C.-F. A tsk-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans. Fuzzy Systems 10 2 (2002) 155-170
10. Kempf R., and Adamy J. Equilibria of recurrent fuzzy systems. Fuzzy Sets and Systems 140 2 (2003) 231-257
11. Kosko B. Fuzzy in Engineering (1992), Prentice-Hall, Englewood Cliffs, NJ
12. Kosmatopoulos E.B., Christodoulou M.A., and Ioannou P.A. Dynamical neural networks that ensure exponential identification error convergence. Neural Networks 10 2 (1997) 299-314
13. J.X. Lee, G. Vukovich, The recurrent dynamic fuzzy logic system and nonlinear system identification, Proc. 38th IEEE Conf. on Decision and Control 5, 1999, pp. 4846-4851.
14. Lin C.-T. Neural Fuzzy Systems: A Neuro-fuzzy Synergism to Intelligent Systems. first ed. (1996), Prentice-Hall, Englewood Cliffs, NJ
15. Mastorocostas P.A., and Theocharis J.B. A recurrent fuzzy-neural model for dynamic system identification. IEEE Trans. Systems Man and Cybernet. Part B Cybernet. 32 2 (2002) 176-190
16. Narendra K., and Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1 (1990) 4-27
17. D.D. Nauck, A. Nürnberger, The evolution of neuro-fuzzy systems, 2005 Annual Meeting of the NAFIPS.
18. Nelles O. Nonlinear System Identification (2001), Springer, Berlin
19. Nerrand O., Roussel P., Personnaz L., Dreyfus G., and Marcos S. Neural networks and non-linear adaptive filtering: unifying concepts and new algorithms. Neural Comput. 5 99 (1993) 165-197
20. Nerrand O., Roussel-Ragot P., Urbani D., Personnaz L., and Dreyfus G. Training recurrent neural networks: why and how? an illustration in dynamical process modeling. IEEE Trans. Neural Networks 5 2 (1994) 178-184
21. Rumelhart D., Hinton G., and Williams R. Learning Internal Representations by Error Propagation (1986), MIT Press, Cambridge (Chapter 8)
22. Sanchez E.N., and Perez J.P. Input-to-state stability (iss) analysis for dynamic neural networks. IEEE Trans. Circuits Systems I Fund. Theory Appl. 46 11 (1999) 1395-1398
23. Sastry P., Santharam G., and Innikrishnan K. Memory neuron networks for identification and control of dynamic systems. IEEE Trans. Neural Networks 5 2 (1994) 306-319
24. Wang L.X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis (1994), Prentice-Hall, Englewood Cliffs, NJ
25. Williams R.J., and Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 2 1 (1989) 270-280
26. Yu W. State-space recurrent fuzzy neural networks for nonlinear system identification. Neural Process. Lett. 22 3 (2005) 391-404
27. W. Yu, X. Li, Fuzzy identification using fuzzy neural network, IEEE Trans. Fuzzy Systems 12 (3).