Cover Image

MLP and Elman recurrent neural network modelling for the TRMS

This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs...

Full description

Saved in:
Bibliographic Details
Main Authors: Toha, Siti Fauziah, Tokhi, M. Osman
Format: Conference or Workshop Item
Language:English
Published: 2008
Subjects:
T173.2 Technological change
Online Access:http://irep.iium.edu.my/7128/1/Siti_CIS_NN_2008.pdf
http://irep.iium.edu.my/7128/
http://dx.doi.org/10.1109/UKRICIS.2008.4798969
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.7128
record_format dspace
spelling my.iium.irep.71282012-10-02T00:07:48Z http://irep.iium.edu.my/7128/ MLP and Elman recurrent neural network modelling for the TRMS Toha, Siti Fauziah Tokhi, M. Osman T173.2 Technological change This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs using Levenberg-Marquardt (LM) training algorithm and Elman recurrent NNs. These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis in modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology. 2008 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/7128/1/Siti_CIS_NN_2008.pdf Toha, Siti Fauziah and Tokhi, M. Osman (2008) MLP and Elman recurrent neural network modelling for the TRMS. In: 7th IEEE International Conference on Cybernetic Intelligent Systems (CIS08), 9-10 September 2008, London, U.K.. http://dx.doi.org/10.1109/UKRICIS.2008.4798969 doi:10.1109/UKRICIS.2008.4798969
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T173.2 Technological change
spellingShingle T173.2 Technological change
Toha, Siti Fauziah
Tokhi, M. Osman
MLP and Elman recurrent neural network modelling for the TRMS
description This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs using Levenberg-Marquardt (LM) training algorithm and Elman recurrent NNs. These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis in modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology.
format Conference or Workshop Item
author Toha, Siti Fauziah
Tokhi, M. Osman
author_facet Toha, Siti Fauziah
Tokhi, M. Osman
author_sort Toha, Siti Fauziah
title MLP and Elman recurrent neural network modelling for the TRMS
title_short MLP and Elman recurrent neural network modelling for the TRMS
title_full MLP and Elman recurrent neural network modelling for the TRMS
title_fullStr MLP and Elman recurrent neural network modelling for the TRMS
title_full_unstemmed MLP and Elman recurrent neural network modelling for the TRMS
title_sort mlp and elman recurrent neural network modelling for the trms
publishDate 2008
url http://irep.iium.edu.my/7128/1/Siti_CIS_NN_2008.pdf
http://irep.iium.edu.my/7128/
http://dx.doi.org/10.1109/UKRICIS.2008.4798969
_version_ 1643605879938875392
score 13.211869

Similar Items