Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes

ISA Transactions

Volumn 50, Issue 2, 2011, Pages 159-169

  Dovžan, Dejan ^a   Škrjanc, Igor ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Clustering; Online recursive identification; Recursive fuzzy c means clustering; Recursive fuzzy identification

Indexed keywords

CLUSTERING ALGORITHMS; COPYING; FAULT DETECTION; FUZZY CLUSTERING; MODEL PREDICTIVE CONTROL; WASTE TREATMENT; WASTEWATER TREATMENT; WATER TREATMENT;

ADAPTIVE NONLINEAR CONTROL; CLUSTERING; FUZZY C MEANS CLUSTERING; ON-LINE IDENTIFICATION METHODS; ONLINE RECURSIVE IDENTIFICATIONS; RECURSIVE FUZZY IDENTIFICATIONS; RECURSIVE LEAST SQUARE (RLS); TAKAGI SUGENO FUZZY MODELS;

FUZZY SYSTEMS;

OXYGEN;

ALGORITHM; ARTICLE; CLUSTER ANALYSIS; COMPUTER SIMULATION; ENGINEERING; FUZZY LOGIC; NONLINEAR SYSTEM; REGRESSION ANALYSIS; SEWAGE; STATISTICS;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTER SIMULATION; ENGINEERING; FUZZY LOGIC; LEAST-SQUARES ANALYSIS; NONLINEAR DYNAMICS; OXYGEN; WASTE DISPOSAL, FLUID;

EID: 79952448247     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isatra.2011.01.004     Document Type: Article

References (41)

1. T.A. Johanson, and R. Murray-Smith Operating regime approach to nonlinear modeling and control R. Murray-Smith, T.A. Johanson, Multiple model approaches to modeling and control 1997 Taylor Francis Hants (UK) 3 72
2. C.L. Hwang, and L.J. Chang Fuzzy neural-based control for nonlinear time-varying delay systems IEEE Trans Syst Man Cybern B 37 6 2007 1471 1485
3. T. Takagi, and M. Sugeno Fuzzy identification of systems and its applications IEEE Trans Syst Man Cybern SMC-15 1985 116 132
4. S.L. Chiu Fuzzy model identification based on cluster estimation J Intell Fuzzy Syst 2 1994 267 278
5. D. Kukolj, and E. Levi Identification of complex systems based on neural and TakagiSugeno fuzzy model IEEE Trans Syst Man Cybern B 34 1 2004 272 282
6. E. Kim, Minkee Park, S. Ji, and Migon Park A new approach to fuzzy modeling IEEE Trans Fuzzy Syst 5 3 1997 328 337 (Pubitemid 127768518)
7. J. Shing, and R. Jang ANFIS: adaptive-network-based fuzzy inference system IEEE Trans Syst Man Cybern 23 3 1993 665 685
8. N. Kasabov Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems Fuzzy Sets and Systems 82 2 1996 665 685
9. P.P. Angelov, and D.P. Filev An approach to online identification of TakagiSugeno fuzzy models IEEE Trans Syst Man Cybern B 34 1 2004 484 497
10. N.K. Kasabov, and Q. Song DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction IEEE Trans Fuzzy Syst 10 2 2002 144 154 (Pubitemid 34554860)
11. G. Leng, G. Prasad, and T.M. McGinnty An on-line algorithm for creating self-organizing fuzzy neural networks Neural Netw 17 10 2004 1477 1493 (Pubitemid 39487143)
12. C.K. Chiang, H.Y. Chung, and J.J. Lin A self-learning fuzzy logic controller using genetic algorithms with reinforcements IEEE Trans Fuzzy Syst 5 3 1997 460 467
13. F. Hoffmann, and G. Pfister Learning of a fuzzy control rule base using messy genetic algorithms Genetic algorithms and soft computing vol. 8 1996 279 305
14. Berenji HR. A reinforcement learning-based architecture for fuzzy logic control. In: Special issue on fuzzy logic and neural networks for pattern recognition and control. vol. 6. no. 2. February 1992. p. 267292.
15. N. Kasabov Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning IEEE Trans Syst Man Cybern B 31 6 2001 902 918 (Pubitemid 34035554)
16. Kasabov N. ECOS: a framework for evolving connectionist systems and the ECO learning paradigm. In: Proc. ICONIP 1998. 1998. p. 122235.
17. Kasabov N. Evolving fuzzy neural networks-algorithms, applications and biological motivation. In: Methodologies for the conceptation, design and application of soft computing. Singapore. 1998. p. 2714.
18. P.P. Angelov Evolving rule-based models: a tool for design of flexible adaptive systems 2002 Springer-Verlag Heidelberg
19. P. Angelov An approach for fuzzy rule-base adaptation using on-line clustering Integr Methods Hybrid Syst 35 3 2004 275 289
20. Yager RR, Filev DP. Learning of fuzzy rules by mountain clustering. In: Proc. SPIE conf. applicat. Fuzzy logic technol. 1993. p. 24654.
21. J. Qiao, and H. Wang A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling Neurocomputing 71 46 2008 564 569 (Pubitemid 351168444)
22. L. Xu, A. Krzyzak, and E. Oja Rival penalized competitive learning for clustering analysis, RBF net, and curve detection IEEE Trans Neural Netw 4 1993 636 649
23. Hai-Jun Rong, N. Sundararajan, Guang-Bin Huang, and P. Saratchandran Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction Fuzzy Sets and Systems 157 9 2006 1260 1275
24. Wang D, Zeng Xiao-Jun, Keane JA. An incremental construction learning algorithm for identification of TS fuzzy systems. In: Proc. FUZZ 2008. IEEE international conference on fuzzy systems 2008. 2008. p. 16606.
25. F.J. Lin, C.H. Lin, and Po.H. Shen Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive IEEE Trans Fuzzy Syst 9 5 2001 751 759
26. S. Wu, and M.J. Er Dynamic fuzzy neural networksa novel approach to function approximation IEEE Trans Syst Man Cybern B 30 2 2000 358 364
27. S. Wu, M.J. Er, and Y. Gao A fast approach for automatic generation of fuzzy rules by generalized dynamicc fuzzy neural networks IEEE Trans Fuzzy Syst 9 4 2001 578 594 (Pubitemid 32933044)
28. C.F. Juang, and C.T. Lin An on-line self-constructing neural fuzzy inference network and its applications IEEE Trans Fuzzy Syst 6 1 1998 12 32
29. C.T. Lin A neural fuzzy control system with structure and parameter learning Fuzzy Sets and Systems 70 1995 183 212
30. S.G. Tzafestas, and K.C. Zikidis NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model IEEE Trans Syst Man Cybern B 31 5 2001 797 802 (Pubitemid 33048059)
31. M.F. Azeem, M. Hanmandlu, and N. Ahmad Structure identification of generalized adaptive neuro-fuzzy inference systems IEEE Trans Fuzzy Syst 11 5 2003 666 681
32. Azeem MF, Manmandlu M, Ahmad N. Modified mountain clustering in dynamic fuzzy modeling. In: 2nd int. conf. inform. technol. 1999. p. 638.
33. J. Patt A resource allocating network for function interpolation Neural Comput 3 2 1991 213 225
34. Deng D, Kasabov N. Evolving self-organizing maps for online learning, data analysis and modeling. In: Proc. IJCNN 2000 neural networks neural computing: new challenges perspectives new millennium. vol. 6. 2000. p. 38.
35. B. Fritzke A growing neural gas network learns topologies Adv Neural Inf Process Syst 7 1995
36. M.C. Mackey, and L. Glass Oscillations and chaos in physiological control systems Science 197 1977 287 289 (Pubitemid 8130447)
37. J.C. Bezdek Pattern recognition with fuzzy objective function algorithms 1981 Plenum Press New York
38. G.C. Goodwin, and K.S. Sin Adaptive filtering prediction and control 1984 Prentice-Hall Upper Saddle River (New York)
39. K.J. strm, and B. Wittenmark Adaptive control 1995 Addison-Wesley New York
40. M. Kumar, R. Stoll, and N. Stoll Deterministic approach to robust adaptive learning of fuzzy models IEEE Trans Syst Man Cybern B 36 4 2006 767 780 (Pubitemid 44178148)
41. krjanc I, Teslic L. Monitoring of waste-water treatment plant using TakagiSugeno fuzzy model. In: Electrotechnical conference. 2008. MELECON 2008. The 14th IEEE Mediterranean. 2008. p. 6770. doi:10.1109/MELCON.2008.4618412.