A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling

Neurocomputing

Volumn 71, Issue 4-6, 2008, Pages 564-569

  Qiao, Junfei ^a   Wang, Huidong ^a,b  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

Forecast model; Fuzzy neural networks; Self organizing; Wastewater treatment system

Indexed keywords

APPROXIMATION THEORY; AUTOMATION; COMPUTER SIMULATION; FORECASTING; IDENTIFICATION (CONTROL SYSTEMS); LEARNING ALGORITHMS; SELF ORGANIZING MAPS; SUPERVISED LEARNING;

FORECAST MODEL; SELF-ORGANIZING CLUSTERING APPROACH; SELF-ORGANIZING FUZZY NEURAL NETWORK;

FUZZY NEURAL NETWORKS;

ACTIVATED SLUDGE; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; FORECASTING; FUZZY LOGIC; FUZZY SYSTEM; LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SIMULATION; WASTE WATER MANAGEMENT;

EID: 38649139150     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.07.026     Document Type: Article

References (21)

1. Berthold M., and Huber K.P. Constructing fuzzy graphs from examples. Int. J. Intell. Data Anal. 3 (1999) 37-53
2. Chen S., Cowan C.F.N., and Grant P.M. Orthogonal least squares learning algorithm for radial basis function network. IEEE Trans. Neural Networks 2 (1991) 302-309
3. Cho K.B., and Wang B.H. Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction. Fuzzy Sets Systems 83 (1996) 325-339
4. Jang J.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (1993) 665-684
5. Jang J.S.R., Sun C.T., and Mizutani E. Neuro-fuzzy and Soft Computing (1997), Prentice-Hall, Englewood Cliffs, NJ
6. Juang C.F., and Lin C.T. An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans. Fuzzy Systems 6 (1998) 12-32
7. Leng G., Prasad G., and McGinnity T.M. An on-line algorithm for creating self-organizing fuzzy neural networks. Neural Networks 17 (2004) 1477-1493
8. Mastorocostas P.A., and Theocharis J.B. A recurrent fuzzy neural model for dynamic system identification. IEEE Trans. Syst. Man Cybern. 32 (2002) 176-190
9. Philip Chen C.L. A rapid supervised learning neural network for function interpolation and approximation. IEEE Trans. Neural Networks 7 (1996) 1220-1230
10. Ran W.L., and Qiao J.F. BOD soft-measuring approach based on PCA time-delay neural networks. Trans. China Electrotech. Soc. 19 (2004) 78-82
11. Rizzi A., Panella M., and Mascioli F.M.F. Adaptive resolution min-max classifiers. IEEE Trans. Neural Networks 13 (2002) 402-414
12. Rubaai A., Ricketts D., and Kankam M.D. Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives. IEEE Trans. Ind. Appl. 38 (2002) 441-447
13. Shann J.J., and Fu H.C. A fuzzy neural networks for rule acquiring on fuzzy control systems. Fuzzy Sets Systems 71 (1995) 345-357
14. Sugeno M., and Kang G.T. Structure identification of fuzzy model. Fuzzy Sets Systems 28 (1988) 15-33
15. J.S. Wang, C.S.G. Lee, Efficient neuro-fuzzy control systems for autonomous underwater vehicle control, in: Proceedings of 2001 IEEE international Conference on Robotics and Automation, 2001, pp. 2986-2991.
16. Wang L.X. Adaptive fuzzy Systems and Control: Design and Stability Analysis (1994), Prentice-Hall, Englewood Cliffs, NJ
17. Wu S., and Er M.J. Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans. Syst. Man Cybern. 30 (2000) 358-364
18. Wu S., Er M.J., and Gao Y. A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans. Fuzzy Systems 9 (2001) 578-594
19. Xu L., Krzyzak A., and Oja E. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Trans. Neural Networks 4 (1993) 636-649
20. Yi X.N., and Zhou Q. Modeling of wastewater treatment process based on improved BP network. Technol. Water Treat. 31 (2005) 21-24
21. Zadeh L.A. Soft computing and fuzzy logic. IEEE Software 11 (1994) 48-56