Comparison Between Multilayer Feedforward Neural Networks And A Radial Basis Function Network To Detect And Locate Leaks In Pipelines Transporting Gas

dc.creatorSantos R.B.
dc.creatorRupp M.
dc.creatorBonzi S.J.
dc.creatorFileti A.M.F.
dc.date2013
dc.date2015-06-25T19:10:32Z
dc.date2015-11-26T15:08:03Z
dc.date2015-06-25T19:10:32Z
dc.date2015-11-26T15:08:03Z
dc.date.accessioned2018-03-28T22:18:29Z
dc.date.available2018-03-28T22:18:29Z
dc.identifier
dc.identifierChemical Engineering Transactions. Italian Association Of Chemical Engineering - Aidic, v. 32, n. , p. 1375 - 1380, 2013.
dc.identifier19749791
dc.identifier10.33032/CET1332230
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879218182&partnerID=40&md5=aebd3c6fbd1ca1aaaeeaf4a0cba89cec
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/88523
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/88523
dc.identifier2-s2.0-84879218182
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1257602
dc.descriptionAn artificial neural network is a technique of artificial intelligence that has the ability to learn from experiences, improving its performance by adapting to the changes in the environment. The main advantages of neural networks are: The possibility of efficient manipulation of large amounts of data and its ability to generalize results. Considering the great potential of this technique, this paper aims to establish a comparison between Multilayer Feedforward - a Multilayer Perceptron network (MLP) with feedforward learning - and a Radial Basis Function Network (RBF). The RBF and MLP networks are usually employed in the same kind of applications (nonlinear mapping approximation and pattern recognition), however their internal calculation structures are different. A comparison was made by using experimental data from a microphone installed inside a galvanized iron pipeline of 60 m length, under various operating conditions. The signal from the microphone coupled to a data acquisition board in a microcomputer was decomposed in different frequency noises. The dynamics of these noises in time were used as inputs to the neural models to locate and determine the magnitude of the leaks (model outputs). The results obtained from the test sets, with leaks caused intentionally, showed that the two neural structures were able to detect and locate leaks in pipes. Nevertheless, the Multilayer Perceptron network showed a slightly better performance. Copyright © 2013, AIDIC Servizi S.r.l.
dc.description32
dc.description
dc.description1375
dc.description1380
dc.descriptionBarradas, I., Garza, L.E., Menendez, R.M., Leak detection in a pipeline using artificial neural networks (2009) 14thlberoamerican Congress on Patter Recognition, pp. 637-644
dc.descriptionBraga, A.P., Carvalho, A.P.L.F., Ludermir, T.B., (2007) Redes Neurais: Teorias e Aplicações, , 2 ed. LTC, Rio de Janeiro
dc.descriptionCaputo, A.C., Pelagagge, P.M., Using neural networks to monitor piping systems (2003) Process Safety Progress, 22 (2), pp. 119-127
dc.descriptionShibata, A., Konichi, M., Abe, Y., Hasegawa, R., Watanable, M., Kamijo, H., Neuro based classification of gas leakage sounds in pipeline (2009) Proceedings of International Conference on Networking, Sensing and Control, pp. 298-302
dc.descriptionYu, H., Xie, T., Paszezynski, S., Wilamowski, B.M., Advantages of radial basis function networks for dynamics system design (2011) IEEE Transactions on Industrial Electronics, 58 (12)
dc.languageen
dc.publisherItalian Association of Chemical Engineering - AIDIC
dc.relationChemical Engineering Transactions
dc.rightsfechado
dc.sourceScopus
dc.titleComparison Between Multilayer Feedforward Neural Networks And A Radial Basis Function Network To Detect And Locate Leaks In Pipelines Transporting Gas
dc.typeActas de congresos


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