Neural network modelling of a dynamic process

Neural network modelling of a dynamic process

This dissertation presents a dynamic modeling based on the data collected continuously on plant input and output variables using Nonlinear Auto Regression with exogenous inputs (NARX) recurrent neural network. The Levengerg-Marquardt learning algorithm was chosen to train the network, as it is faste...

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Main Author: Wu, Youcheng.
Other Authors: Devanathan, Rajagopalan
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:http://hdl.handle.net/10356/3784
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Institution: Nanyang Technological University
id sg-ntu-dr.10356-3784
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spelling sg-ntu-dr.10356-37842023-07-04T15:21:16Z Neural network modelling of a dynamic process Wu, Youcheng. Devanathan, Rajagopalan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems This dissertation presents a dynamic modeling based on the data collected continuously on plant input and output variables using Nonlinear Auto Regression with exogenous inputs (NARX) recurrent neural network. The Levengerg-Marquardt learning algorithm was chosen to train the network, as it is faster than the other learning algorithms. Master of Science (Computer Control and Automation) 2008-09-17T09:37:29Z 2008-09-17T09:37:29Z 2001 2001 Thesis http://hdl.handle.net/10356/3784 Nanyang Technological University application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Wu, Youcheng.
Neural network modelling of a dynamic process
description This dissertation presents a dynamic modeling based on the data collected continuously on plant input and output variables using Nonlinear Auto Regression with exogenous inputs (NARX) recurrent neural network. The Levengerg-Marquardt learning algorithm was chosen to train the network, as it is faster than the other learning algorithms.
author2 Devanathan, Rajagopalan
author_facet Devanathan, Rajagopalan
Wu, Youcheng.
format Theses and Dissertations
author Wu, Youcheng.
author_sort Wu, Youcheng.
title Neural network modelling of a dynamic process
title_short Neural network modelling of a dynamic process
title_full Neural network modelling of a dynamic process
title_fullStr Neural network modelling of a dynamic process
title_full_unstemmed Neural network modelling of a dynamic process
title_sort neural network modelling of a dynamic process
publishDate 2008
url http://hdl.handle.net/10356/3784
_version_ 1772827253513650176

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