A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties

This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. The former is more promising as it combines two existing techniques that are very close in perfor...

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Main Authors: Fatai Adesina, Anifowose, Jane, Labadin, Abdulazeez, Abdulraheem
Format: Proceeding
Language:English
Published: 2011
Subjects:
Online Access:http://ir.unimas.my/id/eprint/8480/1/Fatai%20Anifowose.pdf
http://ir.unimas.my/id/eprint/8480/
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6122085
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spelling my.unimas.ir.84802022-01-04T06:50:42Z http://ir.unimas.my/id/eprint/8480/ A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties Fatai Adesina, Anifowose Jane, Labadin Abdulazeez, Abdulraheem T Technology (General) This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. The former is more promising as it combines two existing techniques that are very close in performance and well known for their computational stability and fast processing. This proposed FNSVM hybrid model benefits from the excellent performance of the least-square-based model-selection algorithm of Functional Networks and the non-linear high-dimensional feature transformation capability that is based on structural risk minimization and Tikhonov regularization properties of SVM. Training and testing the SVM component of the hybrid model with the best and dimensionally-reduced variables from the input data resulted in better performance with higher correlation coefficients, lower root mean square errors and further less execution time than the standard SVM model. A comparison of FN-SVM with the existing FN-T2FL, using the same data and operating environment, showed that the FNSVM is more accurate and consumes less time. 2011 Proceeding NonPeerReviewed text en http://ir.unimas.my/id/eprint/8480/1/Fatai%20Anifowose.pdf Fatai Adesina, Anifowose and Jane, Labadin and Abdulazeez, Abdulraheem (2011) A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties. In: 11th International Conference on Hybrid Intelligent Systems (HIS), 2011, 5-8 Dec. 2011, Melacca. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6122085
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Fatai Adesina, Anifowose
Jane, Labadin
Abdulazeez, Abdulraheem
A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
description This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. The former is more promising as it combines two existing techniques that are very close in performance and well known for their computational stability and fast processing. This proposed FNSVM hybrid model benefits from the excellent performance of the least-square-based model-selection algorithm of Functional Networks and the non-linear high-dimensional feature transformation capability that is based on structural risk minimization and Tikhonov regularization properties of SVM. Training and testing the SVM component of the hybrid model with the best and dimensionally-reduced variables from the input data resulted in better performance with higher correlation coefficients, lower root mean square errors and further less execution time than the standard SVM model. A comparison of FN-SVM with the existing FN-T2FL, using the same data and operating environment, showed that the FNSVM is more accurate and consumes less time.
format Proceeding
author Fatai Adesina, Anifowose
Jane, Labadin
Abdulazeez, Abdulraheem
author_facet Fatai Adesina, Anifowose
Jane, Labadin
Abdulazeez, Abdulraheem
author_sort Fatai Adesina, Anifowose
title A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
title_short A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
title_full A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
title_fullStr A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
title_full_unstemmed A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
title_sort hybrid of functional networks and support vector machine models for the prediction of petroleum reservoir properties
publishDate 2011
url http://ir.unimas.my/id/eprint/8480/1/Fatai%20Anifowose.pdf
http://ir.unimas.my/id/eprint/8480/
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6122085
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score 13.154949