A New Method for the Identification of Hammerstein Model

H. Al-Duwaish; M. Nazmul Karim

doi:10.1016/s0005-1098(97)00105-2

A New Method for the Identification of Hammerstein Model

H. Al-Duwaish
, M. Nazmul Karim^*

^*Corresponding author for this work

Department of Electrical Engineering

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

86 Scopus citations

Abstract

A new method for the identification of the nonlinear Hammerstein model, consisting of a static nonlinear part in cascade with a linear dynamic part, is introduced. The static nonlinear part is modeled by a multilayer feedforward neural network (MFNN), and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed for estimating the weights of the MFNN and the parameters of ARMA model. Simulation examples are included to illustrate the performance of the proposed method.

Original language	English
Pages (from-to)	1871-1875
Number of pages	5
Journal	Automatica
Volume	33
Issue number	10
DOIs	https://doi.org/10.1016/s0005-1098(97)00105-2
State	Published - Oct 1997

Keywords

Identification
Neural nets
Nonlinear control systems
Process control
Recursive estimation
Time-series analysis

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1016/s0005-1098(97)00105-2

Cite this

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title = "A New Method for the Identification of Hammerstein Model",

abstract = "A new method for the identification of the nonlinear Hammerstein model, consisting of a static nonlinear part in cascade with a linear dynamic part, is introduced. The static nonlinear part is modeled by a multilayer feedforward neural network (MFNN), and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed for estimating the weights of the MFNN and the parameters of ARMA model. Simulation examples are included to illustrate the performance of the proposed method.",

keywords = "Identification, Neural nets, Nonlinear control systems, Process control, Recursive estimation, Time-series analysis",

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AU - Karim, M. Nazmul

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AB - A new method for the identification of the nonlinear Hammerstein model, consisting of a static nonlinear part in cascade with a linear dynamic part, is introduced. The static nonlinear part is modeled by a multilayer feedforward neural network (MFNN), and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed for estimating the weights of the MFNN and the parameters of ARMA model. Simulation examples are included to illustrate the performance of the proposed method.

KW - Identification

KW - Neural nets

KW - Nonlinear control systems

KW - Process control

KW - Recursive estimation

KW - Time-series analysis

UR - https://www.scopus.com/pages/publications/0031248950

U2 - 10.1016/s0005-1098(97)00105-2

DO - 10.1016/s0005-1098(97)00105-2

M3 - Article

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VL - 33

SP - 1871

EP - 1875

JO - Automatica

JF - Automatica

IS - 10

ER -

A New Method for the Identification of Hammerstein Model

Abstract

Keywords

ASJC Scopus subject areas

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