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Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets

Adequate data of the dielectric properties of oil palm fruitlets and the development of appropriate models are central to the quest of quality sensing and characterization in the oil palm industry. In this study, an Artificial Neural Network (ANN) was designed, optimized and deployed to model the di...

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Main Authors: Adedayo, Ojo O., Mohd Isa, Maryam, Che Soh, Azura, Abbas, Zulkifly
Format: Article
Language:English
Published: Blue Eyes Intelligence Engineering & Sciences Publication 2014
Online Access:http://psasir.upm.edu.my/id/eprint/36840/1/Comparison%20of%20feed%20forward%20neural%20network%20training%20algorithms%20for%20intelligent%20modeling%20of%20dielectric%20properties%20of%20oil%20palm%20fruitlets.pdf
http://psasir.upm.edu.my/id/eprint/36840/
http://www.ijeat.org/v3i3.php
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spelling my.upm.eprints.368402015-08-24T07:55:21Z http://psasir.upm.edu.my/id/eprint/36840/ Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets Adedayo, Ojo O. Mohd Isa, Maryam Che Soh, Azura Abbas, Zulkifly Adequate data of the dielectric properties of oil palm fruitlets and the development of appropriate models are central to the quest of quality sensing and characterization in the oil palm industry. In this study, an Artificial Neural Network (ANN) was designed, optimized and deployed to model the dielectric phenomena of microwave interacting with oil palm fruitlets within the frequency range of 2-4GHz. The ANN training data were obtained from Open-ended Coaxial Probe (OCP) microwave measurements and the quasi-static admittance model, the ANN was trained with four different training algorithms: Levenberg Marquardt (LM) algorithm, Gradient Descent with Momentum (GDM) algorithm, Resilient Backpropagation (RP) algorithm and Gradient Descent with Adaptive learning rate (GDA) algorithm. The performance of the ANNs in comparison with measurement data showed that the dielectric properties of the samples under test were accurately modeled, and the LM and RP ANNs can be employed for rapid and accurate determination of the dielectric properties of the oil palm fruitlets. Blue Eyes Intelligence Engineering & Sciences Publication 2014-02 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/36840/1/Comparison%20of%20feed%20forward%20neural%20network%20training%20algorithms%20for%20intelligent%20modeling%20of%20dielectric%20properties%20of%20oil%20palm%20fruitlets.pdf Adedayo, Ojo O. and Mohd Isa, Maryam and Che Soh, Azura and Abbas, Zulkifly (2014) Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets. International Journal of Engineering and Advanced Technology, 3 (3). pp. 38-42. ISSN 2249-8958 http://www.ijeat.org/v3i3.php
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Adequate data of the dielectric properties of oil palm fruitlets and the development of appropriate models are central to the quest of quality sensing and characterization in the oil palm industry. In this study, an Artificial Neural Network (ANN) was designed, optimized and deployed to model the dielectric phenomena of microwave interacting with oil palm fruitlets within the frequency range of 2-4GHz. The ANN training data were obtained from Open-ended Coaxial Probe (OCP) microwave measurements and the quasi-static admittance model, the ANN was trained with four different training algorithms: Levenberg Marquardt (LM) algorithm, Gradient Descent with Momentum (GDM) algorithm, Resilient Backpropagation (RP) algorithm and Gradient Descent with Adaptive learning rate (GDA) algorithm. The performance of the ANNs in comparison with measurement data showed that the dielectric properties of the samples under test were accurately modeled, and the LM and RP ANNs can be employed for rapid and accurate determination of the dielectric properties of the oil palm fruitlets.
format Article
author Adedayo, Ojo O.
Mohd Isa, Maryam
Che Soh, Azura
Abbas, Zulkifly
spellingShingle Adedayo, Ojo O.
Mohd Isa, Maryam
Che Soh, Azura
Abbas, Zulkifly
Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
author_facet Adedayo, Ojo O.
Mohd Isa, Maryam
Che Soh, Azura
Abbas, Zulkifly
author_sort Adedayo, Ojo O.
title Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
title_short Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
title_full Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
title_fullStr Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
title_full_unstemmed Comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
title_sort comparison of feed forward neural network training algorithms for intelligent modeling of dielectric properties of oil palm fruitlets
publisher Blue Eyes Intelligence Engineering & Sciences Publication
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/36840/1/Comparison%20of%20feed%20forward%20neural%20network%20training%20algorithms%20for%20intelligent%20modeling%20of%20dielectric%20properties%20of%20oil%20palm%20fruitlets.pdf
http://psasir.upm.edu.my/id/eprint/36840/
http://www.ijeat.org/v3i3.php
_version_ 1643831844040343552
score 13.211869

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