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Conference Paper: Modelling of river discharges using neural networks derived from support vector regression
Title | Modelling of river discharges using neural networks derived from support vector regression |
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Authors | |
Keywords | Computers Artificial intelligence mathematics |
Issue Date | 2003 |
Publisher | IEEE. |
Citation | IEEE International Fuzzy Systems Conference Proceedings, St. Louis, Missouri, USA, 25-28 May 2003, v. 2, p. 1321-1326 How to Cite? |
Abstract | Neural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls. |
Persistent Identifier | http://hdl.handle.net/10722/46674 |
ISSN | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choy, KY | en_HK |
dc.contributor.author | Chan, CW | en_HK |
dc.date.accessioned | 2007-10-30T06:55:37Z | - |
dc.date.available | 2007-10-30T06:55:37Z | - |
dc.date.issued | 2003 | en_HK |
dc.identifier.citation | IEEE International Fuzzy Systems Conference Proceedings, St. Louis, Missouri, USA, 25-28 May 2003, v. 2, p. 1321-1326 | en_HK |
dc.identifier.issn | 1544-5615 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/46674 | - |
dc.description.abstract | Neural networks are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. Amongst the neural networks, Associative Memory Networks (AMNs) are often used, since they are less computation intensive, and yet good generalization results can be obtained. However, this can only be achieved if the structure of the AMNs is suitably chosen. An approach to choose the structure of the AMNs is to use the Support Vectors (SVs) obtained from the Support Vector Machines. The SVs are obtained from a constrained optimization for a given data set and an error bound. For convenience, this class of AMNs is referred to as the Support Vector Neural Networks (SVNNs). In this paper, the modelling of river discharges with rainfall as input using the SVNN is presented, from which the nonlinear dynamic relationship between rainfall and river discharges is obtained. The prediction of river discharges from the SVNN can give early warning of severe river discharges when there are heavy rainfalls. | en_HK |
dc.format.extent | 374189 bytes | - |
dc.format.extent | 5145 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | text/plain | - |
dc.language | eng | en_HK |
dc.publisher | IEEE. | en_HK |
dc.relation.ispartof | IEEE International Conference on Fuzzy Systems | en_HK |
dc.rights | ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
dc.subject | Computers | en_HK |
dc.subject | Artificial intelligence mathematics | en_HK |
dc.title | Modelling of river discharges using neural networks derived from support vector regression | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1544-5615&volume=2&spage=1321&epage=1326&date=2003&atitle=Modelling+of+river+discharges+using+neural+networks+derived+from+support+vector+regression | en_HK |
dc.identifier.email | Chan, CW: mechan@hkucc.hku.hk | en_HK |
dc.identifier.authority | Chan, CW=rp00088 | en_HK |
dc.description.nature | published_or_final_version | en_HK |
dc.identifier.doi | 10.1109/FUZZ.2003.1206622 | en_HK |
dc.identifier.scopus | eid_2-s2.0-0038170239 | en_HK |
dc.identifier.hkuros | 79465 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-0038170239&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 2 | en_HK |
dc.identifier.spage | 1321 | en_HK |
dc.identifier.epage | 1326 | en_HK |
dc.identifier.scopusauthorid | Choy, KY=7005477038 | en_HK |
dc.identifier.scopusauthorid | Chan, CW=7404814060 | en_HK |
dc.identifier.issnl | 1544-5615 | - |