Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1392
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Ling, SH | - |
dc.creator | Lam, HK | - |
dc.creator | Leung, FHF | - |
dc.creator | Lee, YS | - |
dc.date.accessioned | 2014-12-11T08:26:23Z | - |
dc.date.available | 2014-12-11T08:26:23Z | - |
dc.identifier.isbn | 0-7803-7810-5 | - |
dc.identifier.uri | http://hdl.handle.net/10397/1392 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
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. | en_US |
dc.rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | en_US |
dc.subject | Neural Network | en_US |
dc.subject | Neural Fuzzy Network | en_US |
dc.subject | Genetic Algorithm | en_US |
dc.title | A genetic algorithm based fuzzy-tuned neural network | en_US |
dc.type | Conference Paper | en_US |
dc.description.otherinformation | Author name used in this publication: F. H. F. Leung | en_US |
dc.description.otherinformation | Author name used in this publication: Y. S. Lee | en_US |
dc.description.otherinformation | Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering | en_US |
dc.description.otherinformation | Refereed conference paper | en_US |
dcterms.abstract | This paper presents a fuzzy-tuned neural network, which is trained by the genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a novel neuron model with two activation functions is employed. The parameters of the proposed network are tuned by GA with arithmetic crossover and non-uniform mutation. Some application examples are given to illustrate the merits of the proposed network. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | FUZZ-IEEE 2003 : proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA, p. 220-225 | - |
dcterms.issued | 2003 | - |
dc.identifier.isi | WOS:000183448800038 | - |
dc.identifier.scopus | 2-s2.0-0038818485 | - |
dc.relation.ispartofbook | FUZZ-IEEE 2003 : proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA | - |
dc.relation.conference | IEEE International Conference on Fuzzy Systems [FUZZ] | - |
dc.identifier.rosgroupid | r14875 | - |
dc.description.ros | 2002-2003 > Academic research: refereed > Refereed conference paper | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Fuzzy-tuned neural network_03.pdf | 335.9 kB | Adobe PDF | View/Open |
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