Extended fuzzy function model with stable learning methods for online system identification

International Journal of Adaptive Control and Signal Processing

Volumn 25, Issue 2, 2011, Pages 168-182

  Beyhan, Selami ^a   Alci, Musa ^b  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b EGE UNIVERSITY   (Turkey)

Author keywords

adaptive learning rate; extended fuzzy function model; input to state stability; online system identification

Indexed keywords

ACCURATE MODELING; ADAPTIVE LEARNING RATES; ASYMPTOTIC CONVERGENCE; BOUNDEDNESS; BOX-JENKINS; COMPUTATIONALLY EFFICIENT; EXTENDED FUZZY FUNCTION MODEL; FURNACE SYSTEMS; FUZZY FUNCTION; GRADIENT-DESCENT; INPUT-OUTPUT BEHAVIOR; INPUT-TO-STATE STABILITY; LEARNING METHODS; MODELING ERRORS; NON-LINEAR DYNAMIC SYSTEMS; NON-LINEAR MODEL; NON-LINEAR SYSTEM IDENTIFICATION; NONLINEAR DISCRETE-TIME SYSTEMS; NUMERICAL SIMULATION; OFF-LINE MODELING; ON-LINE IDENTIFICATION; ONLINE SYSTEM IDENTIFICATION; PRIOR KNOWLEDGE; RECURSIVE LEAST SQUARES; SYSTEM IDENTIFICATIONS;

DIGITAL CONTROL SYSTEMS; DISCRETE TIME CONTROL SYSTEMS; DYNAMICAL SYSTEMS; FURNACES; FUZZY SET THEORY; GAS FURNACES; IDENTIFICATION (CONTROL SYSTEMS); INTELLIGENT SYSTEMS; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; NONLINEAR ANALYSIS; NONLINEAR DYNAMICAL SYSTEMS; ONLINE SYSTEMS; REAL TIME CONTROL; SYSTEM STABILITY;

MATHEMATICAL MODELS;

EID: 79251602005     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.1214     Document Type: Article

References (33)

1. Wang L-X,. A Course in Fuzzy Systems and Control. Prentice-Hall Inc.: U.S.A., 1997.
2. Jang J-SR, Sun CT, Mizutani E,. Neuro Fuzzy and Soft Computing. Prentice-Hall: U.S.A., 1997.
3. Wang L-X, Mendel JM,. Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Transactions on Neural Networks 1992; 5 (3): 807-814.
4. Türken IB,. Fuzzy functions with LSE. Applied Soft Computing 2008; 8: 1178-1188.
5. Türken IB,. Mixed fuzzy functions. Annual Meeting of the North American Fuzzy Information Processing Society. NAFIPS 2008, New York, U.S.A., 2008; 1-5.
6. CelikyÄlmaz A, Türken IB, Aktas R, Doganay MM, Ceylan NB,. A new classifier design with fuzzy functions. Lecture Notes in Computer Science, 2007; 4482: 136-143.
7. Asli C, Turksen IB,. Increasing accuracy of two class pattern recognition with improved fuzzy functions. Expert Systems with Applications 2009; 36: 1337-1354.
8. Türken IB, Celikyilmaz A,. Comparison of fuzzy functions with fuzzy rule base approaches. International Journal of Fuzzy Systems 2006; 8 (3): 137-149.
9. Türken IB,. Review of fuzzy system models with an emphasis on fuzzy functions. Transactions of the Institute of Measurement and Control 2009; 31 (1): 7-31.
10. Beyhan S, Alci M,. Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identification. Applied Soft Computing 2010; 10: 439-444.
11. Lewis FL, Yeildirek A, Liu K,. Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Transactions on Neural Networks 1996; 7 (2): 388-399.
12. Yoo SJ, Choi YH, Park JB,. Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach. IEEE Transactions on Circuits and Systems-I: Regular Papers 2006; 53 (6): 1381-1394.
13. Zerkaoui S, Druaux F, Leclercq E, Lefebvre D,. Stable adaptive control with recurrent neural networks for square MIMO non-linear systems. Engineering Applications of Artificial Intelligence 2009; 22: 702-717.
14. Treesatayapun C,. Nonlinear discrete-time controller with unknown systems identification based on fuzzy rules emulated network. Applied Soft Computing 2010; 10: 390-397.
15. Laxmidhar B, Swagat K, Awhan P,. On adaptive learning rate that guarantees convergence in feedforward networks. IEEE Transactions on Neural Networks 2006; 17 (5): 1116-1125.
16. Shoorehdeli MA, Teshnehlab M, Sedigh AKi, Khanesar MA,. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Applied Soft Computing 2009; 9: 833-850.
17. Yu W, Li X,. On-line fuzzy modeling with structure and parameter learning. Expert Systems with Applications 2009; 36: 7484-7492.
18. Sheel S, Varshney T, Varshney R,. Accelerated learning in MLP using adaptive learning rate with momentum coefficient. Second International Conference on Industrial and Information Systems, ICIIS 2007, Sri Lanka, August 2007; 8-11.
19. Beyhan S, Alci M,. Stable modeling based control methods using a new RBF network. ISA Transactions 2010. DOI: 10.1016/j.isatra.2010.04.005.
20. Folly KA, Venayagamoorthy GK,. Effects of learning rate on the performance of the population based incremental learning algorithm. Proceedings of International Joint Conference on Neural Networks, Atlanta, GA, U.S.A., 14-19 June 2009.
21. Wang CH, Liu HL, Lin CT,. Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm. IEEE Transactions on Systems, Man, CyberneticsPart B 2001; 31: 467-475.
22. Purwar S, Kar IN, Jha AN,. On-line system identification of complex systems using Chebyshev neural networks. Applied Soft Computing 2007; 7: 364-372.
23. Simon D,. Training fuzzy systems with the extended Kalman filter. Fuzzy Sets and Systems 2002; 132: 189-199.
24. Kim KJ, Park JB, Choi YH,. The adaptive learning rates of extended Kalman filter based training algorithm for wavelet neural networks. MICAI 2006, LNAI 4293, 2006; 327-337.
25. Bayard DS, Spanos J, Rahman Z,. A result on exponential tracking error convergence and persistent excitation. IEEE Transactions on Automatic Control 1998; 43 (9): 1334-1338.
26. Spooner JT, Maggiore M, Ordonez R, Passino KM,. Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques. Wiley: New York, 2002.
27. Aadaleesan P, Miglan N, Sharma R, Saha P,. Nonlinear system identification using Wiener type Laguerrewavelet network model. Chemical Engineering Science 2008; 63: 3932-3941.
28. Liu Y, Wang H, Yu c J, Li P,. Selective recursive kernel learning for on-line identification of nonlinear systems with NARX form. Journal of Process Control 2010; 20: 181-194.
29. Kim E, Park M, Ji S,. A new approach to fuzzy modeling. IEEE Transactions on Fuzzy Systems 1997; 5: 328-337.
30. Kukolj D, Levi E,. Identification of complex systems based on neural and Takagi-Sugeno fuzzy model. IEEE Transactions On Systems, Man, And CyberneticsPart B: Cybernetics 2004; 34 (1): 272-282.
31. Gonzalez-Olvera MA, Tang Y,. A new recurrent neurofuzzy network for identification of dynamic systems. Fuzzy Sets and Systems 2007; 158: 1023-1035.
32. Mastorocostas PA, Theocharis JB,. A recurrent fuzzy-neural model for dynamic system identification. IEEE Transactions On Systems, Man And CyberneticsPart B: Cybernetics 2002; 32 (2): 176-190.
33. Juang C-F,. A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Transactions on Fuzzy Systems 2002; 10 (2): 155-170.