Fast automatic two-stage nonlinear model identification based on the extreme learning machine

Neurocomputing

Volumn 74, Issue 16, 2011, Pages 2422-2429

  Deng, Jing ^a   Li, Kang ^a   Irwin, George W ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Extreme learning machine; Leave one out cross validation; RBF networks; Two stage stepwise selection

Indexed keywords

ALTERNATIVE APPROACH; AUTOMATIC CONSTRUCTION; CONSTRUCTION PROCEDURES; EXHAUSTIVE SEARCH; EXPERT EXPERIENCE; EXTREME LEARNING MACHINE; FAST LEARNING; GAUSSIAN FUNCTIONS; GENERALIZATION CAPABILITY; GRADIENT BASED; LEAVE-ONE-OUT CROSS VALIDATIONS; NEWTON'S METHODS; NON-LINEAR PARAMETERS; NONLINEAR MODEL IDENTIFICATION; NONLINEAR PROBLEMS; NOVEL ALGORITHM; ORTHOGONAL LEAST SQUARES; SIMULATION EXAMPLE; SYSTEM MODELS; TWO STAGE; TWO-STAGE STEPWISE SELECTION;

ALGORITHMS; LEAST SQUARES APPROXIMATIONS; NEWTON-RAPHSON METHOD; NONLINEAR SYSTEMS; NUMERICAL METHODS; STRUCTURE (COMPOSITION);

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; AUTOMATION; COMPUTER SIMULATION; CONTROLLED STUDY; EXTREME LEARNING MACHINE; LEAVE ONE OUT CROSS VALIDATION; MACHINE LEARNING; MATHEMATICAL COMPUTING; NONLINEAR SYSTEM; ORTHOGONAL LEAST SQUARE; PRIORITY JOURNAL; RADIAL BASED FUNCTION;

EID: 80051589759     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.11.035     Document Type: Article

References (19)

1. Li K., Peng J.X., Bai E.W. A two-stage algorithm for identification of nonlinear dynamic systems. Automatica 2006, 42(7):1189-1197.
2. Nelles O. Nonlinear System Identification 2001, Springer.
3. Huang G., Zhu Q., Siew C. Extreme learning machine: theory and applications. Neurocomputing 2006, 70:489-501.
4. Huang G., Chen L., Siew C. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17(4):879-892.
5. Huang G., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70(16-18):3056-3062.
6. Miller A. Subset Selection in Regression 2002, CRC Press.
7. Hong X., Mitchell R.J., Chen S., Harris C.J., Li K., Irwin G.W. Model selection approaches for non-linear system identification: a review. International Journal of Systems Science 2008, 39(10):925-946.
8. Chen S., Billings S.A., Luo W. Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control 1989, 50(5):1873-1896.
9. Chen S., Cowan C.F.N., Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 1991, 2(2):302-309.
10. Li K., Peng J.X., Irwin G.W. A fast nonlinear model identification method. IEEE Transactions on Automatic Control 2005, 50(8):1211-1216.
11. Chen S., Chng E.S., Alkadhimi K. Regularized orthogonal least squares algorithm for constructing radial basis function networks. International Journal of Control 1996, 64(5):829-837.
12. D.J. Du, K. Li, M.R. Fei, G.W. Irwin, A new locally regularised recursive method for the selection of RBF neural network centres, in: R. Walter (Ed.), Proceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, Saint-Malo, France, 2009.
13. Hong X., Sharkey P.M., Warwick K. Automatic nonlinear predictive model construction algorithm using forward regression and the press statistic. IEE Proceedings: Control Theory and Applications 2003, 150(3):245-254.
14. Sherstinsky A., Picard R.W. On the efficiency of the orthogonal least squares training method for radial basis function networks. IEEE Transactions on Neural Networks 1996, 7(1):195-200.
15. Mao K.Z., Billings S.A. Algorithms for minimal model structure detection in nonlinear dynamic system identification. International Journal of Control 2007, 68(2):311-330.
16. Li K., Huang G., Ge S. A Fast Construction Algorithm for Feedforward Neural Networks 2010, Springer Verlag.
17. Lan Y., Soh Y., Huang G. Two-stage extreme learning machine for regression. Neurocomputing 2010, 73(16-18):3028-3038.
18. Ljung L. System Identification: Theory for the User 1987, Prentice Hall, Cliffs, NJ.
19. Peng J.X., Li K., Huang D.S. A hybrid forward algorithm for RBF neural network construction. IEEE Transactions on Neural Networks 2006, 17(6):1439-1451.