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Ensemble Dual Algorithm Using RBF Recursive Learning for Partial Linear Network

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.

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Official URL: http://www.springerlink.com/content/gp232210662n42...

Abstract

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.

Item Type:Book Section
Impact Factor:10.1007/978-3-642-20042-7<sub>2</sub>6
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Centre of Excellence > Center for Intelligent Signal and Imaging Research
Departments > Electrical & Electronic Engineering
ID Code:6788
Deposited By: Dr Vijanth Sagayan Asirvadam
Deposited On:21 Nov 2011 06:30
Last Modified:28 Mar 2014 13:21

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