[1] Li Shihua*, Li Qi, Li Jie,. Identification of Hammerstein Model Using Hybrid Neural Networks [J]. Journal of Southeast University (English Edition), 2001, 17 (1): 26-30. [doi:10.3969/j.issn.1003-7985.2001.01.007]

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Identification of Hammerstein Model Using Hybrid Neural Networks()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

17

Issue:

2001 1

Page:

26-30

Research Field:

Automation

Publishing date:

2001-06-30

Info

Title:

Identification of Hammerstein Model Using Hybrid Neural Networks

Author(s):

Li Shihua^1*;  Li Qi²;  Li Jie³

¹Department of Automatic Control Engineering, Southeast University, Nanjing 210096, China
²Research Institute of Automation, Southeast University, Nanjing 210096, China
³Nanjing Automation Research Institute, Nanjing 210003, China

Keywords:

neural networks;  nonlinear systems identification;  Hammerstein model

PACS:

TP14

DOI:

10.3969/j.issn.1003-7985.2001.01.007

Abstract:

The identification problem of Hammerstein model with extension to the multi-input multi-output(MIMO)case is studied. The proposed identification method uses a hybrid neural network(HNN)which consists of a multi-layer feed-forward neural network(MFNN)in cascade with a linear neural network(LNN). A unified back-propagation(BP)algorithm is proposed to estimate the weights and the biases of the MFNN and the LNN simultaneously. Numerical examples are provided to show the efficiency of the proposed method.

References:

[1] K. S. Narendra, and P. G. Gallman, An iterative method for the identification of nonlinear system using the Hammerstein model, IEEE Trans Automat Control, vol.11, no.2, pp. 546-550, 1966
[2] F. H. I. Chang, and R. Luus, A noniterative method for identification using Hammerstein model, IEEE Trans Automat Control, vol. 16, no.5, pp. 464-468, 1971
[3] W. Greblicki, and M. Pawlak, Identification of discrete Hammerstein systems using kernel regression estimates, IEEE Trans Automat Control, vol. 31, no. 1, pp. 74-77, 1986
[4] W. Greblicki, and M. Pawlak, Nonparametric identification of Hammerstein systems, IEEE Trans Inform Theory, vol.35, no.2, pp. 409-418, 1989
[5] H. Al-Duwaish, and M. N. Karim, A new method for the identification of Hammerstein model, Automatica, vol. 33, no. 10, pp.1871-1875, 1997
[6] K. S. Narendra, and K. Parthasarathy, Identification and control of dynamic systems using neural networks, IEEE Trans Neural Networks, vol.1, no.1, pp. 4-27, 1990
[7] S. Li, F. Wu, and Q. Li, Identification of Wiener model using dynamic artificial neural networks, Control Theory and Applications, vol.17, no.1, pp. 92-95, 2000
[8] L. Ljung, and T. Soderstrom, Theory and practice of recursive identification, MIT Press, Cambridge, MA, 1983

Memo

Memo:

* Born in 1975, male, graduate.

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