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Layer-recurrent network in identifying a nonlinear system

Layer-Recurrent Network (LRN) is a dynamic neural network and is seen as a promising black box model in identifying a nonlinear system injected with nonlinear input signal. In this paper, LRN will be used to identify a nonlinear, state space 3-axis satellite model. Open loop identification is applie...

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Main Authors:	Nordin F.H., Nagi F.H.
Other Authors:	25930510500
Format:	Conference paper
Published:	2023
Subjects:	Layer-Recurrent Network (LRN) Nonlinear input Nonlinear system identification Satellite attitude Mathematical models Metropolitan area networks Navigation Neural networks Nonlinear systems Satellites Black box models Dynamic Neural networks Input and outputs Mean-squared values Open loop identifications Prior knowledges Recurrent networks Satellite dynamics Satellite models Simulated datums State Space models State spaces Identification (control systems)
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id	my.uniten.dspace-30964
record_format	dspace
spelling	my.uniten.dspace-309642023-12-29T15:56:48Z Layer-recurrent network in identifying a nonlinear system Nordin F.H. Nagi F.H. 25930510500 56272534200 Layer-Recurrent Network (LRN) Nonlinear input Nonlinear system identification Satellite attitude Mathematical models Metropolitan area networks Navigation Neural networks Nonlinear systems Satellites Black box models Dynamic Neural networks Input and outputs Layer-Recurrent Network (LRN) Mean-squared values Nonlinear input Nonlinear system identification Open loop identifications Prior knowledges Recurrent networks Satellite attitude Satellite dynamics Satellite models Simulated datums State Space models State spaces Identification (control systems) Layer-Recurrent Network (LRN) is a dynamic neural network and is seen as a promising black box model in identifying a nonlinear system injected with nonlinear input signal. In this paper, LRN will be used to identify a nonlinear, state space 3-axis satellite model. Open loop identification is applied and methodology on nonlinear system identification is presented where the best pair of input and output data is first measured. Using the simulated data, six LRN models are used to identify the satellite dynamics. It is shown that only 200 epochs are needed to train a network to converge to a reasonable mean squared value (mse). LRN output is then compared with the state space model where it shows that LRN model is capable to produce similar results as the state space satellite model without knowing the system's state and prior knowledge of the system. Final 2023-12-29T07:56:48Z 2023-12-29T07:56:48Z 2008 Conference paper 10.1109/ICCAS.2008.4694674 2-s2.0-58149087554 https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149087554&doi=10.1109%2fICCAS.2008.4694674&partnerID=40&md5=c1cdf90349296f35a40489eebe771c07 https://irepository.uniten.edu.my/handle/123456789/30964 4694674 387 391 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Layer-Recurrent Network (LRN) Nonlinear input Nonlinear system identification Satellite attitude Mathematical models Metropolitan area networks Navigation Neural networks Nonlinear systems Satellites Black box models Dynamic Neural networks Input and outputs Layer-Recurrent Network (LRN) Mean-squared values Nonlinear input Nonlinear system identification Open loop identifications Prior knowledges Recurrent networks Satellite attitude Satellite dynamics Satellite models Simulated datums State Space models State spaces Identification (control systems)
spellingShingle	Layer-Recurrent Network (LRN) Nonlinear input Nonlinear system identification Satellite attitude Mathematical models Metropolitan area networks Navigation Neural networks Nonlinear systems Satellites Black box models Dynamic Neural networks Input and outputs Layer-Recurrent Network (LRN) Mean-squared values Nonlinear input Nonlinear system identification Open loop identifications Prior knowledges Recurrent networks Satellite attitude Satellite dynamics Satellite models Simulated datums State Space models State spaces Identification (control systems) Nordin F.H. Nagi F.H. Layer-recurrent network in identifying a nonlinear system
description	Layer-Recurrent Network (LRN) is a dynamic neural network and is seen as a promising black box model in identifying a nonlinear system injected with nonlinear input signal. In this paper, LRN will be used to identify a nonlinear, state space 3-axis satellite model. Open loop identification is applied and methodology on nonlinear system identification is presented where the best pair of input and output data is first measured. Using the simulated data, six LRN models are used to identify the satellite dynamics. It is shown that only 200 epochs are needed to train a network to converge to a reasonable mean squared value (mse). LRN output is then compared with the state space model where it shows that LRN model is capable to produce similar results as the state space satellite model without knowing the system's state and prior knowledge of the system.
author2	25930510500
author_facet	25930510500 Nordin F.H. Nagi F.H.
format	Conference paper
author	Nordin F.H. Nagi F.H.
author_sort	Nordin F.H.
title	Layer-recurrent network in identifying a nonlinear system
title_short	Layer-recurrent network in identifying a nonlinear system
title_full	Layer-recurrent network in identifying a nonlinear system
title_fullStr	Layer-recurrent network in identifying a nonlinear system
title_full_unstemmed	Layer-recurrent network in identifying a nonlinear system
title_sort	layer-recurrent network in identifying a nonlinear system
publishDate	2023
_version_	1806428246330310656
score	13.222552

Layer-recurrent network in identifying a nonlinear system

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