Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network

Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network

This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control s...

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Published in:Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012
Main Author: Yunan I.; Yassin I.M.; Adnan S.F.S.; Rahiman M.H.F.
Format: Conference paper
Language:English
Published: 2012
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861537546&doi=10.1109%2fCSPA.2012.6194779&partnerID=40&md5=63cd12214de8b1ceb4d809f763079d11
id 2-s2.0-84861537546
spelling 2-s2.0-84861537546
Yunan I.; Yassin I.M.; Adnan S.F.S.; Rahiman M.H.F.
Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
2012
Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012


10.1109/CSPA.2012.6194779
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861537546&doi=10.1109%2fCSPA.2012.6194779&partnerID=40&md5=63cd12214de8b1ceb4d809f763079d11
This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control signal, and outputs depicting temperatures inside the distillation column. One Step Ahead (OSA) model fitting and residual tests demonstrated that the RBFNN-based NARX model was able to approximate the system well, while satisfying all validation criterias. © 2012 IEEE.


English
Conference paper
author Yunan I.; Yassin I.M.; Adnan S.F.S.; Rahiman M.H.F.
spellingShingle Yunan I.; Yassin I.M.; Adnan S.F.S.; Rahiman M.H.F.
Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
author_facet Yunan I.; Yassin I.M.; Adnan S.F.S.; Rahiman M.H.F.
author_sort Yunan I.; Yassin I.M.; Adnan S.F.S.; Rahiman M.H.F.
title Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
title_short Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
title_full Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
title_fullStr Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
title_full_unstemmed Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
title_sort Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
publishDate 2012
container_title Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012
container_volume
container_issue
doi_str_mv 10.1109/CSPA.2012.6194779
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861537546&doi=10.1109%2fCSPA.2012.6194779&partnerID=40&md5=63cd12214de8b1ceb4d809f763079d11
description This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control signal, and outputs depicting temperatures inside the distillation column. One Step Ahead (OSA) model fitting and residual tests demonstrated that the RBFNN-based NARX model was able to approximate the system well, while satisfying all validation criterias. © 2012 IEEE.
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issn
language English
format Conference paper
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