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<p>Created page with &quot; == Abstract == The physical and operational properties of pipelines vary greatly. There is thus no universally applicable method, external or internal, which possesses all t...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> The physical and operational properties of pipelines vary greatly. There is thus no universally applicable method, external or internal, which possesses all the features and the functionality required for a perfect leak detection performance. The authors of this paper know quite well that traditional methods, in a low uncertainty environment, overcome artificial intelligence methods of leak detection systems. If one considers the real world as a creator of uncertainties, neural networks and fuzzy systems emerge as important promising technologies for the development of leak detection systems. In this work, we propose a method for constructing ensembles of ANNs for pipeline leak detection. The results obtained in our experiments were satisfactory.Copyright © 2008 by ASME<br /> <br /> <br /> == Original document ==<br /> <br /> The different versions of the original document can be found in:<br /> <br /> * [http://pdfs.semanticscholar.org/772e/a7a4d174bb2f6a7257e73640668ecf1152b8.pdf http://pdfs.semanticscholar.org/772e/a7a4d174bb2f6a7257e73640668ecf1152b8.pdf]<br /> <br /> * [http://asmedigitalcollection.asme.org/IPC/proceedings-pdf/doi/10.1115/IPC2008-64664/2717393/739_1.pdf http://asmedigitalcollection.asme.org/IPC/proceedings-pdf/doi/10.1115/IPC2008-64664/2717393/739_1.pdf],<br /> : [http://dx.doi.org/10.1115/ipc2008-64664 http://dx.doi.org/10.1115/ipc2008-64664]<br /> <br /> * [http://www.professores.uff.br/fcbernardini/papers/Bicharra_Ferraz_Bernardini-Pipeline2008.pdf http://www.professores.uff.br/fcbernardini/papers/Bicharra_Ferraz_Bernardini-Pipeline2008.pdf],<br /> : [https://asmedigitalcollection.asme.org/IPC/proceedings/IPC2008/48579/739/336443 https://asmedigitalcollection.asme.org/IPC/proceedings/IPC2008/48579/739/336443],<br /> : [https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1640428 https://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1640428],<br /> : [http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1640428 http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1640428],<br /> : [https://www.addlabs.uff.br/Novo_Site_ADDLabs/images/documentos/publicacoes/publicacoes_pdf/trabalhos_anais_congresso/2008/20130809154919_2008%20-%20Artificial%20neural%20networks%20ensemble%20used%20for%20pipeline%20leak%20detection%20systems.pdf https://www.addlabs.uff.br/Novo_Site_ADDLabs/images/documentos/publicacoes/publicacoes_pdf/trabalhos_anais_congresso/2008/20130809154919_2008%20-%20Artificial%20neural%20networks%20ensemble%20used%20for%20pipeline%20leak%20detection%20systems.pdf],<br /> : [http://www.addlabs.uff.br/Novo_Site_ADDLabs/images/documentos/publicacoes/publicacoes_pdf/trabalhos_anais_congresso/2008/20130809154919_2008%20-%20Artificial%20neural%20networks%20ensemble%20used%20for%20pipeline%20leak%20detection%20systems.pdf http://www.addlabs.uff.br/Novo_Site_ADDLabs/images/documentos/publicacoes/publicacoes_pdf/trabalhos_anais_congresso/2008/20130809154919_2008%20-%20Artificial%20neural%20networks%20ensemble%20used%20for%20pipeline%20leak%20detection%20systems.pdf],<br /> : [https://academic.microsoft.com/#/detail/2114470142 https://academic.microsoft.com/#/detail/2114470142]</div>

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