Mimo wiener model identification using radial basis functions neural networks

Syed Saad Azhar Ali; Hussain N. Al-Duwaish

Mimo wiener model identification using radial basis functions neural networks

Syed Saad Azhar Ali^*, Hussain N. Al-Duwaish

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents a novel technique for the identification of the nonlinear multi-input multi-output (MIMO) Wiener Model, consisting of linear dynamics in cascade with static nonlinearities. The ARMA/RBFNN structure presented in [1] is exteneded for MIMO case. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model based on least mean squares (LMS). The identification of both linear and nonlinear parts is done simultaneously as compared to the other indirect approaches.

Original language	English
Article number	412-164
Pages (from-to)	74-78
Number of pages	5
Journal	Proceedings of the IASTED International Conference on Modelling, Identification and Control
Volume	23
State	Published - 2004

Keywords

LMS
MIMO
RBFNN
Wiener Model

ASJC Scopus subject areas

Software
Modeling and Simulation
Computer Science Applications

Cite this

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

abstract = "This paper presents a novel technique for the identification of the nonlinear multi-input multi-output (MIMO) Wiener Model, consisting of linear dynamics in cascade with static nonlinearities. The ARMA/RBFNN structure presented in [1] is exteneded for MIMO case. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model based on least mean squares (LMS). The identification of both linear and nonlinear parts is done simultaneously as compared to the other indirect approaches.",

keywords = "LMS, MIMO, RBFNN, Wiener Model",

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

year = "2004",

language = "English",

volume = "23",

pages = "74--78",

journal = "Proceedings of the IASTED International Conference on Modelling, Identification and Control",

issn = "1025-8973",

publisher = "ACTA Press",

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TY - JOUR

T1 - Mimo wiener model identification using radial basis functions neural networks

AU - Ali, Syed Saad Azhar

AU - Al-Duwaish, Hussain N.

PY - 2004

Y1 - 2004

N2 - This paper presents a novel technique for the identification of the nonlinear multi-input multi-output (MIMO) Wiener Model, consisting of linear dynamics in cascade with static nonlinearities. The ARMA/RBFNN structure presented in [1] is exteneded for MIMO case. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model based on least mean squares (LMS). The identification of both linear and nonlinear parts is done simultaneously as compared to the other indirect approaches.

AB - This paper presents a novel technique for the identification of the nonlinear multi-input multi-output (MIMO) Wiener Model, consisting of linear dynamics in cascade with static nonlinearities. The ARMA/RBFNN structure presented in [1] is exteneded for MIMO case. The proposed algorithm estimates the weights of the RBFNN and the coefficients of ARMA model based on least mean squares (LMS). The identification of both linear and nonlinear parts is done simultaneously as compared to the other indirect approaches.

KW - LMS

KW - MIMO

KW - RBFNN

KW - Wiener Model

UR - https://www.scopus.com/pages/publications/11844264824

M3 - Conference article

AN - SCOPUS:11844264824

SN - 1025-8973

VL - 23

SP - 74

EP - 78

JO - Proceedings of the IASTED International Conference on Modelling, Identification and Control

JF - Proceedings of the IASTED International Conference on Modelling, Identification and Control

M1 - 412-164

ER -

Mimo wiener model identification using radial basis functions neural networks

Abstract

Keywords

ASJC Scopus subject areas

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