Non-parametric identification of generalized Hammerstein models

Hosam E. Emara-Shabaik; Kamal A.F. Moustafa

doi:10.1080/00207729808929500

Non-parametric identification of generalized Hammerstein models

Hosam E. Emara-Shabaik, Kamal A.F. Moustafa

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

The Hammerstein model is considered in a generalized form, where its nonlinear element can have multi-inputs and a finite memory. The identification of the multi-input finite memory nonimearity and the impulse response sequence of the model is treated using a non-parametric approach. A numerical example is given. The identification results of the example illustrate the effectiveness of the developed technique.

Original language	English
Pages (from-to)	91-93
Number of pages	3
Journal	International Journal of Systems Science
Volume	29
Issue number	1
DOIs	https://doi.org/10.1080/00207729808929500
Publication status	Published - Jan 1998
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications

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10.1080/00207729808929500

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abstract = "The Hammerstein model is considered in a generalized form, where its nonlinear element can have multi-inputs and a finite memory. The identification of the multi-input finite memory nonimearity and the impulse response sequence of the model is treated using a non-parametric approach. A numerical example is given. The identification results of the example illustrate the effectiveness of the developed technique.",

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Non-parametric identification of generalized Hammerstein models

Abstract

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