A new method for the identification of Hammerstein model
type: Item
instance of: scholarly article
title: A new method for the identification of Hammerstein model
zbMATH Open document ID: 0894.93012
DOI: 10.1016/S0005-1098(97)00105-2
author: H. Al-Duwaish
author: M. Nazmul Karim
published in: Automatica
publication date: 1997-12-17T00:00:00Z
review text: An identification algorithm using a hybrid model that consists of a multilayer feedforward neural network (used to identify the static nonlinearity) in series with a parametric ARMA model of fixed order (implemented for modeling the linear dynamics) is proposed for identification of discrete-time SISO and MIMO Hammerstein systems. To update the weights of the neural network and adjust the parameter estimates of the ARMA model simultaneously, a recursive updating procedure is developed, based on the backpropagation algorithm and the recursive least squares. Performance of the method is examined by numerical examples.
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93A99
zbMATH DE Number: 1097589
zbMATH Keywords: identification
zbMATH Keywords: neural network
zbMATH Keywords: parametric ARMA model
zbMATH Keywords: Hammerstein systems
zbMATH Keywords: backpropagation
zbMATH Keywords: recursive least squares
reviewed by: Zygmunt Hasiewicz
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1097589