NDLI: Optimizing Neural Networks for Leak Monitoring in Pipelines

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Ayman, H. Kassem Ihab, G. Adam
Copyright Year	2004
Abstract	Feedforward neural networks can be used for nonlinear dynamic modeling. Although the basic principle of employing such networks is straightforward, the problem of selecting the training data set and the network topology is not a trivial task. This paper examines the use of genetic algorithm optimization techniques to optimize the neural network. The paper presents the results of studies on the effect of number of neurons and input combination method on the performance of neural networks and the application of this study to improve leak monitoring in pipelines. The neural networks examined in this study do not use the sensor reading directly as in conventional neural networks but combine it using polynomial type laws to produce hybrid inputs. The optimization technique tries to find the best polynomial laws (input combination) to reduce network size, head variation effect, and optimize network performance. The resulting networks show superior performance and use fewer numbers of neurons.
Sponsorship	Pressure Vessels and Piping Division
Starting Page	57
Ending Page	61
Page Count	5
File Format	PDF
ISBN	0791846830
DOI	10.1115/PVP2004-3005
Volume Number	Risk and Reliability and Evaluation of Components and Machinery
Conference Proceedings	ASME/JSME 2004 Pressure Vessels and Piping Conference
Language	English
Publisher Date	2004-07-25
Publisher Place	San Diego, California, USA
Access Restriction	Subscribed
Subject Keyword	Leak detection Pipelines Artificial neural networks Genetic algorithms Leakage
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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