Input-output modelling with decomposed neuro-fuzzy ARX model

Neurocomputing

Volumn 71, Issue 4-6, 2008, Pages 875-884

  Golob, Marjan ^a   Tovornik, Boris ^a  

^a UNIVERSITY OF MARIBOR   (Slovenia)

Author keywords

Fuzzy ARX model; Input output modelling; Neuro fuzzy system

Indexed keywords

DYNAMICAL SYSTEMS; FUZZY INFERENCE; IDENTIFICATION (CONTROL SYSTEMS); INPUT OUTPUT PROGRAMS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; NONLINEAR SYSTEMS; OPTIMIZATION; SOFT COMPUTING;

BOX-JENKINS DATA SET; INPUT-OUTPUT MODELLING;

FUZZY NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; FUZZY SYSTEM; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

EID: 38649091085     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.02.011     Document Type: Article

References (31)

1. A. Bastian, Identifying fuzzy rule-based models using soft computing, in: Proceedings of EUFIT'98, vol. 1 Aachen, Germany, 1998, pp. 589-593.
2. Billings S.A. Identification of nonlinear systems-a survey. IEE Proc. 127 (1980) 272-285
3. Box G.E.P., and Jenkins G.M. Time Series Analysis, Forecasting and Control (1970), Holden Day, San Francisco
4. P. J. Costa Branco, J.A. Dante, A new algorithm for on-line relational identification of nonlinear dynamic systems, in: Proceedings of the Second IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'93), vol. 2, 1993, pp. 1173-1178.
5. Czogala E., and Pedrycz W. On identification in fuzzy systems and its applications in control problems. Fuzzy Sets Syst. 6 (1981) 73-83
6. Ding Y., and Ying S. Shao H. Necessary conditions on minimal system configuration for general miso mamdani fuzzy system as universal approximators. IEEE Trans. Syst. Man Cybernet-B: Cybernet. 30 (2000) 857-864
7. E. Elsamahy, M. Mahfouf, D. Linkens, A hybrid intelligent closed-loop model for exploration of cardiovascular interactions, in: Proceedings of the fourth International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, 2003, pp. 165-168.
8. Frayman Y., and Wang L. A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes. Soft Comput. 6 (2002) 244-253
9. Golob M. Decomposition of a fuzzy controller based on the inference break-up method. Intell. Data Anal. 3 (1999) 127-137
10. M. Golob, B. Tovornik, Identification of non-linear dynamic systems with decomposed fuzzy models, in: Proceedings of the 2000 IEEE International Conference on Systems, Man and Cybernetics, October 8-11, 2000, Nashville, 2000, pp. 3520-3525.
11. Lee C.C. Fuzzy logic in control systems: fuzzy logic controller-Part I and II. IEEE Trans. Syst. Man Cybernet SMC-20 (1990) 404-435
12. Ljung L., and Soderstrom T. Theory and Practice of Recursive Identification (1983), MIT Press, London
13. In: Murray-Smith R., and Johansen T.A. (Eds). Multiple Model Approaches to Modeling and Control (1997), Taylor & Francis, London
14. Nobuhara H., Hirota K., Sessa S., and Pedrycz W. Efficient decomposition methods of fuzzy relation and their application to image decomposition. Appl. Soft Comput. 5 (2005) 399-408
15. Pedrycz W. Identification algorithm in fuzzy relational systems. Fuzzy Sets Syst. 13 (1984) 153-167
16. Ridley J.N., Shaw I.S., and Kruger J.J. Probabilistic fuzzy model for dynamic systems. Electron. Lett. 24 14 (1988) 890-892
17. Rumelhart D.E., Hinton G.E., and Williams R.K. Learning representations by backpropagation errors. Nature 323 9 (1986) 533-536
18. Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybernet. 23 (1993) 665-685
19. J.-S. R. Jang, Neuro-fuzzy modeling for dynamic system identification, in: Proceedings of the 1996 Asian Fuzzy Systems Symposium, 1996, pp. 320-325.
20. G. Serra, C. Bottura, An IV-QR algorithm for neuro-fuzzy multivariable online identification, IEEE Trans. Fuzzy Syst., Digital Object Identifier, accepted for future publication doi:10.1109/TFUZZ.2006.879997.
21. Silva F.M., and Almeida L.B. Speeding up backpropagation. In: Eckmiller R. (Ed). Advanced Neural Computers (1990), Amsterdam, North-Holland 151-160
22. Sugeno M., and Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1 1 (1993) 7-31
23. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man Cybernet. SMC-15 (1985) 116-132
24. Tong R.M. The evolution of fuzzy models derived from experimental data. Fuzzy Sets Syst. 4 (1980) 1-12
25. Wang L.X., and Mendel J.M. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Networks NN-3 NN-3 (1992) 807-814
26. Wang L., and Frayman Y. A dynamically-generated fuzzy neural network and its application to torsional vibration control of tandem cold rolling mill spindles. Eng. Appl. Artificial Intell. 15 (2003) 541-550
27. Wang L.-X. Design and analysis of fuzzy identifiers of nonlinear dynamic systems. IEEE Trans. Automat. Control 40 1 (1995) 11-23
28. Xu C.W., and Lu Y.Z. Fuzzy model identification and self-learning for dynamic systems. IEEE Trans. Syst. Man Cybern. 17 4 (1987) 683-689
29. Sisman-Yilmaz N.A., Alpaslan F.N., and Jain L. ANFIS_unfolded_in_time for multivariate time series forcasting. Neurocomputing 61 (2004) 139-168
30. Zweiri Y.H., Whidborn J.F., and Seneviratne L.D. A three-term backpropagation algorithm. Neurocomputing 50 (2003) 305-318
31. Zweiri Y.H., Whidborn J.F., Seneviratne L.D., and Althoefer K. Stability analysis of a three-term backpropagation algorithm. Neural Networks 18 (2005) 1341-1347