An automated hammerstein recurrent neural network for dynamic applications

Proceedings of the IASTED International Conference on Computational Intelligence

Volumn 2005, Issue , 2005, Pages 193-198

  Chen, Yen Ping ^a   Wang, Jeen Shing ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

And parameter initialization optimization; Hammerstein models; Order determination; Recurrent networks; State space representations

Indexed keywords

COMPUTER SIMULATION; LEARNING ALGORITHMS; NONLINEAR SYSTEMS; OPTIMIZATION; STATE SPACE METHODS;

HAMMERSTEIN MODELS; ORDER DETERMINATION; PARAMETER INITIALIZATION; STATE SPACE REPRESENTATIONS;

RECURRENT NEURAL NETWORKS;

EID: 27944454175     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. rd Ann. Allerton Conf. Circuit and System Theory, pp. 449-459, 1965.
2. K. S. Narendra and P. G. Gallman, "An iterative method for the identification of nonlinear systems using a Hammerstein model," IEEE Trans. on Automatic Control, vol. 11, pp. 546-550, 1966.
3. E. J. Dempsey, and D. T. Westwick, "Identification of Hammerstein models with cubic spline nonlinearities," IEEE Trans. Biomed. Eng., vol. 51, no. 2, pp. 237-245, 2004.
4. A. Janczak, "Neural network approach for identification of Hammerstein systems," International Journal of Control, vol. 76, no. 17, pp. 1749-1766, 2003.
5. A. Janczak, "Identification of a class of nonlinear systems using neural networks," Proc. of the International Conference on MMAR, pp. 697-702, 1995.
6. H. Al-Duwaish, M. Nazuml Karim, and V. Chandrasekar, "Hammerstein model identification by multiplayer feedforward neural networks," International Journal of Systems Science, pp. 49-54, 1997.
7. T. Lin, B. G. Horne, C. L. Giles, and S. Y. Kung, "What to remember: How memory order affects the performance of NARX neural networks," IEEE World Conference on Computational Intelligence, pp. 1051-1056, 1998.
8. X. He and H. Asada, "A new method for identifying orders of input-output models for nonlinear dynamic systems," Proc. of the ACC, pp. 2520-2523, 1993.
9. Y. F. Yam and T. W. S. Chow, "Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients," IEEE Trans. on Neural Networks, vol.2, no. 2, pp. 430-434, 2001.
10. L. Ljung, System Identification: Theory for the User, Prentice-Hall, Jan. 1999.
11. T. McKelvey, Identification of State-Space Models from Time and. Frequency Data, Ph.D. thesis, Department of Electrical Engineering, Linkoping University, Linkoping, Sweden, 1995.
12. Y.-P. Chen and J.-S. Wang, "A novel recurrent neural network with minimal representation for dynamic system identification," Proc. of 2004 IEEE International Joint Conference on Neural Networks, Budapest, Hungary, pp. 849-854, July 25-29, 2004.
13. C. F. Juang and C. T. Lin, "A recurrent self-organizing neural fuzzy inference network," IEEE Trans, on Neural Networks, vol. 10, no. 4, pp. 828-845, 1999.