Hammerstein model identification using radial basis functions neural networks

Hussain N. Al-Duwaish; Syed Saad Azhar Ali

doi:10.1007/3-540-44668-0_131

Hammerstein model identification using radial basis functions neural networks

Hussain N. Al-Duwaish
, Syed Saad Azhar Ali

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model.

Original language	English
Title of host publication	Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings
Editors	Kurt Hornik, Georg Dorffner, Horst Bischof
Publisher	Springer Verlag
Pages	951-956
Number of pages	6
ISBN (Print)	3540424865, 9783540446682
DOIs	https://doi.org/10.1007/3-540-44668-0_131
State	Published - 2001

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2130
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/3-540-44668-0_131

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Al-Duwaish, H. N., & Ali, S. S. A. (2001). Hammerstein model identification using radial basis functions neural networks. In K. Hornik, G. Dorffner, & H. Bischof (Eds.), Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings (pp. 951-956). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2130). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_131

Al-Duwaish, Hussain N. ; Ali, Syed Saad Azhar. / Hammerstein model identification using radial basis functions neural networks. Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings. editor / Kurt Hornik ; Georg Dorffner ; Horst Bischof. Springer Verlag, 2001. pp. 951-956 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Hammerstein model identification using radial basis functions neural networks",

abstract = "A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model.",

author = "Al-Duwaish, \{Hussain N.\} and Ali, \{Syed Saad Azhar\}",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Al-Duwaish, HN & Ali, SSA 2001, Hammerstein model identification using radial basis functions neural networks. in K Hornik, G Dorffner & H Bischof (eds), Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2130, Springer Verlag, pp. 951-956. https://doi.org/10.1007/3-540-44668-0_131

Hammerstein model identification using radial basis functions neural networks. / Al-Duwaish, Hussain N.; Ali, Syed Saad Azhar.
Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings. ed. / Kurt Hornik; Georg Dorffner; Horst Bischof. Springer Verlag, 2001. p. 951-956 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2130).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model.

AB - A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and the parameters of the ARMA model.

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Al-Duwaish HN, Ali SSA. Hammerstein model identification using radial basis functions neural networks. In Hornik K, Dorffner G, Bischof H, editors, Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings. Springer Verlag. 2001. p. 951-956. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44668-0_131

Hammerstein model identification using radial basis functions neural networks

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