عرض للأخصائي: Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network

Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network

There are many ways for gas (or high-pressure hazardous liquid) be transferred from one place to another. However, pipelines are considered as the fastest and the cheapest means to convey such flammable substances, for example natural gas, methane, ethane, benzene, propane and etc. Unavoidably, the...

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المؤلفون الرئيسيون:	Md Akib, Afif, Saad, Nordin, Asirvadam, Vijanth
مؤلفون آخرون:	Nguyen, Ngoc
التنسيق:	Book Section
منشور في:	Springer-Verlag 2011
الموضوعات:	TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science
الوصول للمادة أونلاين:	http://www.springerlink.com/content/gp232210662n4243/fulltext.pdf http://eprints.utp.edu.my/6788/
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

id	my.utp.eprints.6788
record_format	eprints
spelling	my.utp.eprints.67882014-03-28T13:21:09Z Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network Md Akib, Afif Saad, Nordin Asirvadam, Vijanth TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science There are many ways for gas (or high-pressure hazardous liquid) be transferred from one place to another. However, pipelines are considered as the fastest and the cheapest means to convey such flammable substances, for example natural gas, methane, ethane, benzene, propane and etc. Unavoidably, the pipelines may be affected by interference from third parties, for example human error while under its operation. Consequently, any accidental releases of gas that may occur due to the failure of the pipeline implies the risk that must be controlled. Therefore, it is necessary to evaluate the safety of the pipeline with quantitative risk assessment. Relative mass released of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms for updating the linear weight. Radial basis function (RBF) is used to define the non-linear weight of the partial linear network. A new learning algorithm called the ensemble dual algorithm for estimating the mass-flow rate of the flow after leakage is proposed. Simulations with pressure liquid storage tanks problems have tested this learning approach. Springer-Verlag Nguyen, Ngoc Kim, Chong-Gun Janiak, Adam 2011 Book Section PeerReviewed http://www.springerlink.com/content/gp232210662n4243/fulltext.pdf Md Akib, Afif and Saad, Nordin and Asirvadam, Vijanth (2011) Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network. In: Intelligent Information and Database Systems. Lecture Notes in Computer Science, 6592 . Springer-Verlag, Berlin , pp. 252-261. http://eprints.utp.edu.my/6788/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
topic	TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering QA75 Electronic computers. Computer science Md Akib, Afif Saad, Nordin Asirvadam, Vijanth Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network
description	There are many ways for gas (or high-pressure hazardous liquid) be transferred from one place to another. However, pipelines are considered as the fastest and the cheapest means to convey such flammable substances, for example natural gas, methane, ethane, benzene, propane and etc. Unavoidably, the pipelines may be affected by interference from third parties, for example human error while under its operation. Consequently, any accidental releases of gas that may occur due to the failure of the pipeline implies the risk that must be controlled. Therefore, it is necessary to evaluate the safety of the pipeline with quantitative risk assessment. Relative mass released of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms for updating the linear weight. Radial basis function (RBF) is used to define the non-linear weight of the partial linear network. A new learning algorithm called the ensemble dual algorithm for estimating the mass-flow rate of the flow after leakage is proposed. Simulations with pressure liquid storage tanks problems have tested this learning approach.
author2	Nguyen, Ngoc
author_facet	Nguyen, Ngoc Md Akib, Afif Saad, Nordin Asirvadam, Vijanth
format	Book Section
author	Md Akib, Afif Saad, Nordin Asirvadam, Vijanth
author_sort	Md Akib, Afif
title	Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network
title_short	Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network
title_full	Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network
title_fullStr	Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network
title_full_unstemmed	Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network
title_sort	ensemble dual algorithm using rbf recursive learning for partial linear network
publisher	Springer-Verlag
publishDate	2011
url	http://www.springerlink.com/content/gp232210662n4243/fulltext.pdf http://eprints.utp.edu.my/6788/
_version_	1738655520661700608
score	13.251813

Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network

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