Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1277
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorChau, KW-
dc.date.accessioned2014-12-11T08:24:05Z-
dc.date.available2014-12-11T08:24:05Z-
dc.identifier.isbn978-3-540-25914-5-
dc.identifier.urihttp://hdl.handle.net/10397/1277-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesLecture notes in computer science ; v. 3498-
dc.rights© Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.en_US
dc.subjectParticle swarm optimizationen_US
dc.subjectArtificial neural networksen_US
dc.subjectAlgorithmsen_US
dc.subjectBackpropagationen_US
dc.subjectBenchmarkingen_US
dc.subjectCost effectivenessen_US
dc.subjectAlgaeen_US
dc.subjectTolo Harbouren_US
dc.titleA split-step PSO algorithm in prediction of water quality pollutionen_US
dc.typeBook Chapteren_US
dc.description.otherinformationAuthor name used in this publication: Kwokwing Chauen_US
dc.identifier.doi10.1007/11427469_164-
dcterms.abstractIn order to allow the key stakeholders to have more float time to take appropriate precautionary and preventive measures, an accurate prediction of water quality pollution is very significant. Since a variety of existing water quality models involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptrons to forecast real-time algal bloom dynamics in Tolo Harbour of Hong Kong. The advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the usual PSO algorithm, it attains a higher accuracy in a much shorter time.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn J Wang, X Liao & Z Yi (Eds.), Advances in neural networks--ISNN 2005 : Second International Symposium on Neural Networks, Chongqing, China, May 30-June 1, 2005 : proceedings, p. 1034-1039. Berlin: Springer, 2005-
dcterms.issued2005-
dc.identifier.isiWOS:000230167700164-
dc.identifier.scopus2-s2.0-24944477705-
dc.relation.ispartofbookAdvances in neural networks--ISNN 2005 : Second International Symposium on Neural Networks, Chongqing, China, May 30-June 1, 2005 : proceedings-
dc.relation.conferenceInternational Symposium on Neural Networks [ISNN]-
dc.publisher.placeBerlinen_US
dc.identifier.rosgroupidr21665-
dc.description.ros2004-2005 > Academic research: refereed > Publication in refereed journal-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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