Ensemble model of Artificial Neural Networks with randomized number of hidden neurons

Conventional artificial intelligence techniques and their hybrid models are incapable of handling several hypotheses at a time. The limitation in the performance of certain techniques has made the ensemble learning paradigm a desirable alternative for better predictions. The petroleum industry stand...

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Main Authors:	Fatai Adesina, Anifowose, Jane, Labadin
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://ir.unimas.my/8474/ http://ir.unimas.my/8474/ http://ir.unimas.my/8474/1/Ensemble%20model%20of%20non-linear%20feature%20selection-based%20Extreme%20Learning%20Machine%20%28abstract%29.pdf

id	unimas-8474
recordtype	eprints
spelling	unimas-84742015-08-05T01:09:15Z http://ir.unimas.my/8474/ Ensemble model of Artificial Neural Networks with randomized number of hidden neurons Fatai Adesina, Anifowose Jane, Labadin TJ Mechanical engineering and machinery Conventional artificial intelligence techniques and their hybrid models are incapable of handling several hypotheses at a time. The limitation in the performance of certain techniques has made the ensemble learning paradigm a desirable alternative for better predictions. The petroleum industry stands to gain immensely from this learning methodology due to the persistent quest for better prediction accuracies of reservoir properties for improved hydrocarbon exploration, production, and management activities. Artificial Neural Networks (ANN) has been applied in petroleum engineering but widely reported to be lacking in global optima caused mainly by the great challenge involved in the determination of optimal number of hidden neurons. This paper presents a novel ensemble model of ANN that uses a randomized algorithm to generate the number of hidden neurons in the prediction of petroleum reservoir properties. Ten base learners of the ANN model were created with each using a randomly generated number of hidden neurons. Each learner contributed in solving the problem and a single ensemble solution was evolved. The performance of the ensemble model was evaluated using standard evaluation criteria. The results showed that the performance of the proposed ensemble model is better than the average performance of the individual base learners. This study is a successful proof of concept of randomization of the number of hidden neurons and demonstrated the great potential for the application of this learning paradigm in petroleum reservoir characterization. 2013 Conference or Workshop Item NonPeerReviewed text en http://ir.unimas.my/8474/1/Ensemble%20model%20of%20non-linear%20feature%20selection-based%20Extreme%20Learning%20Machine%20%28abstract%29.pdf Fatai Adesina, Anifowose and Jane, Labadin (2013) Ensemble model of Artificial Neural Networks with randomized number of hidden neurons. In: 8th International Conference on Information Technology in Asia (CITA), 2013, 1-4 July 2013, Kota Samarahan. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6637562
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Sarawak
building	UNIMAS Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Fatai Adesina, Anifowose Jane, Labadin Ensemble model of Artificial Neural Networks with randomized number of hidden neurons
description	Conventional artificial intelligence techniques and their hybrid models are incapable of handling several hypotheses at a time. The limitation in the performance of certain techniques has made the ensemble learning paradigm a desirable alternative for better predictions. The petroleum industry stands to gain immensely from this learning methodology due to the persistent quest for better prediction accuracies of reservoir properties for improved hydrocarbon exploration, production, and management activities. Artificial Neural Networks (ANN) has been applied in petroleum engineering but widely reported to be lacking in global optima caused mainly by the great challenge involved in the determination of optimal number of hidden neurons. This paper presents a novel ensemble model of ANN that uses a randomized algorithm to generate the number of hidden neurons in the prediction of petroleum reservoir properties. Ten base learners of the ANN model were created with each using a randomly generated number of hidden neurons. Each learner contributed in solving the problem and a single ensemble solution was evolved. The performance of the ensemble model was evaluated using standard evaluation criteria. The results showed that the performance of the proposed ensemble model is better than the average performance of the individual base learners. This study is a successful proof of concept of randomization of the number of hidden neurons and demonstrated the great potential for the application of this learning paradigm in petroleum reservoir characterization.
format	Conference or Workshop Item
author	Fatai Adesina, Anifowose Jane, Labadin
author_facet	Fatai Adesina, Anifowose Jane, Labadin
author_sort	Fatai Adesina, Anifowose
title	Ensemble model of Artificial Neural Networks with randomized number of hidden neurons
title_short	Ensemble model of Artificial Neural Networks with randomized number of hidden neurons
title_full	Ensemble model of Artificial Neural Networks with randomized number of hidden neurons
title_fullStr	Ensemble model of Artificial Neural Networks with randomized number of hidden neurons
title_full_unstemmed	Ensemble model of Artificial Neural Networks with randomized number of hidden neurons
title_sort	ensemble model of artificial neural networks with randomized number of hidden neurons
publishDate	2013
url	http://ir.unimas.my/8474/ http://ir.unimas.my/8474/ http://ir.unimas.my/8474/1/Ensemble%20model%20of%20non-linear%20feature%20selection-based%20Extreme%20Learning%20Machine%20%28abstract%29.pdf
first_indexed	2018-09-06T15:34:08Z
last_indexed	2018-09-06T15:34:08Z
_version_	1610872833851785216

Ensemble model of Artificial Neural Networks with randomized number of hidden neurons

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