A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks

Neurocomputing

Volumn 72, Issue 16-18, 2009, Pages 3818-3829

  Wang, Ning ^a,b   Er, Meng Joo ^b   Meng, Xianyao ^a  

^a DALIAN MARITIME UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Extended Kalman filter (EKF); Fuzzy neural network (FNN); Growing criterion; Online self organizing

Indexed keywords

ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM; BENCH-MARK PROBLEMS; CHAOTIC TIME SERIES PREDICTION; FREE PARAMETERS; FUNCTION APPROXIMATION; FUZZY NEURAL NETWORK (FNN); GROWING AND PRUNING; GROWING CRITERION; HIDDEN NEURONS; HIDDEN UNITS; LEARNING SPEED; MINIMAL RESOURCE ALLOCATION NETWORK; NETWORK STRUCTURES; NON-LINEAR DYNAMIC SYSTEMS; NOVEL STRUCTURES; ONLINE LEARNING; ONLINE SELF-ORGANIZING; ONLINE SEQUENTIAL EXTREME LEARNING MACHINE; ORTHOGONAL LEAST SQUARES; PARAMETER LEARNING; PRUNING STRATEGY; REAL-WORLD; REGRESSION PROBLEM; RESOURCE ALLOCATION NETWORKS; SELF-ORGANIZING FUZZY NEURAL NETWORK; SELF-ORGANIZING SCHEME; SIMULATION RESULT;

APPROXIMATION ALGORITHMS; CELLULAR RADIO SYSTEMS; CHAOTIC SYSTEMS; CONTROL THEORY; EDUCATION; EXTENDED KALMAN FILTERS; FUZZY INFERENCE; FUZZY SYSTEMS; INTERNET; LEARNING ALGORITHMS; LEARNING SYSTEMS; PLANNING; POSITION CONTROL; QUEUEING NETWORKS; RADIAL BASIS FUNCTION NETWORKS; RESOURCE ALLOCATION; SPEECH RECOGNITION;

FUZZY NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FUZZY SYSTEM; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; NERVE CELL GROWTH; NONLINEAR SYSTEM; ONLINE SYSTEM; PRIORITY JOURNAL; REGRESSION ANALYSIS; STATISTICAL MODEL; TIME SERIES ANALYSIS;

EID: 69249220427     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.05.006     Document Type: Article

References (23)

1. Chao C.T., Chen Y.J., and Teng C.C. Simplification of fuzzy-neural systems using similarity analysis. IEEE Trans. Syst., Man, Cybern., B, Cybern. 26 (1996) 344-354
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) 1411-1423
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 Syst. 83 (1996) 325-339
4. Gao Y., and Er M.J. Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems. IEEE Trans. Fuzzy Syst. 11 (2003) 12-32
5. Gao Y., and Er M.J. NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets Syst. 150 (2005) 331-350
6. Gao Y., and Er M.J. An intelligent adaptive control scheme for postsurgical blood pressure regulation. IEEE Trans. Neural Networks 16 (2005) 475-483
7. Huang G.-B., Saratchandran P., and Sundararajan N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans. Syst., Man, Cybern., B, Cybern. 34 (2004) 2284-2292
8. Huang G.-B., Saratchandran P., and Sundararajan N. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Trans. Neural Networks 16 (2005) 57-67
9. Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst., Man, Cybern. 23 (1993) 665-684
10. Jang J.-S.R., Sun C.T., and Mizutani E. Neuro-Fuzzy and Soft Computing (1997), Prentice-Hall, Englewood Cliffs, NJ
11. Juang C.-F., and Lin C.-T. An on-line self-constructing neural fuzzy inference network and its applications. IEEE Trans. Fuzzy Syst. 6 (1998) 12-32
12. Kadirkamanathan V., and Niranjan M. A function estimation approach to sequential learning with neural networks. Neural Comput. 5 (1993) 954-975
13. Leng G., McGinnity T.M., and Prasad G. An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. Fuzzy Sets Syst. 150 (2005) 211-243
14. Liang N.-Y., Huang G.-B., Saratchandran P., and Sundararajan N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Networks 17 (2006) 1411-1423
15. Mitra S., and Hayashi Y. Neuro-fuzzy rule generation: survey in soft computing framework. IEEE Trans. Neural Networks 11 (2000) 748-768
16. Platt J. A resource-allocating network for function interpolation. Neural Comput. 3 (1991) 213-225
17. Rojas I., Pomares H., Bernier J.L., Ortega J., Pino B., Pelayo F.J., and Prieto A. Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing 42 (2002) 267-285
18. Salmeron M., Ortega J., Puntonet C.G., and Prieto A. Improved RAN sequential prediction using orthogonal techniques. Neurocomputing 41 (2001) 153-172
19. Wang L.X. Adaptive Fuzzy Systems and Control: Design and Stability Analysis (1994), Prentice-Hall, Englewood Cliffs, NJ
20. Wu S.-Q., and Er M.J. Dynamic fuzzy neural networks-a novel approach to function approximation. IEEE Trans. Syst., Man, Cybern., B, Cybern. 30 (2000) 358-364
21. Wu S.-Q., Er M.J., and Gao Y. A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans. Fuzzy Syst. 9 (2001) 578-594
22. Yingwei L., Sundararajan N., and Saratchandran P. A sequential learning scheme for function approximation using minimal radial basis function (RBF) neural networks. Neural Comput. 9 (1997) 461-478
23. Yingwei L., Sundararajan N., and Saratchandran P. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks 9 (1998) 308-318