Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70065
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorFeng, BO-
dc.creatorLiu, JNK-
dc.date.accessioned2017-11-13T02:16:36Z-
dc.date.available2017-11-13T02:16:36Z-
dc.identifier.isbn978-3-540-25913-8-
dc.identifier.isbn978-3-540-32067-8-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10397/70065-
dc.description2nd International Symposium on Neural Networks (ISNN 2005), Chongqing, China, May 30 - June 1, 2005en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.titleAn adaptive neural network classifier for tropical cyclone prediction using a two-layer feature selectoren_US
dc.typeConference Paperen_US
dc.identifier.spage399-
dc.identifier.epage404-
dc.identifier.volume3497-
dc.identifier.doi10.1007/11427445_65-
dcterms.abstractWe are in need of more accurate, automated prediction and classification methods for the determination of weather patterns all over the world, especially for the identification of severe weather patterns such as tropical cyclones (TC). They help to discover hazardous meteorological phenomena, providing an early warning to save people’s lives and properties. In this paper, we propose an adaptive neural network classifier to predict the intensity of a tropical cyclone based on associated features, which is preprocessed by a two-layer feature selector. A binary trigger is used to adjust the neural network topology adaptively when necessary by controlling the validity of each hidden node. Experimental results show that our proposed classifier is a preferable one on learning speed and predictive accuracy comparing to other neural algorithms.-
dcterms.bibliographicCitationLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2005, v. 3497, p. 399-404-
dcterms.isPartOfLecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)-
dcterms.issued2005-
dc.relation.conferenceInternational Symposium on Neural Networks [ISNN]-
dc.identifier.eissn1611-3349-
dc.identifier.rosgroupidr21886-
dc.description.ros2004-2005 > Academic research: refereed > Refereed conference paper-
Appears in Collections:Conference Paper
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