DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, SG | ko |
dc.contributor.author | Oh, SK | ko |
dc.contributor.author | Kim, MS | ko |
dc.contributor.author | Lee, Ju-Jang | ko |
dc.date.accessioned | 2009-01-12T02:08:05Z | - |
dc.date.available | 2009-01-12T02:08:05Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2002-01 | - |
dc.identifier.citation | ELECTRONICS LETTERS, v.38, no.1, pp.34 - 35 | - |
dc.identifier.issn | 0013-5194 | - |
dc.identifier.uri | http://hdl.handle.net/10203/8281 | - |
dc.description.abstract | The evolutionary structure optimisation (ESO) method for Gaussian radial basis function (RBF) networks has already been presented by the authors. Here, they improve the ESO method in its mutation operator and apply it to a mixture of experts (ME) for modelling and predicting nonlinear time series. The ME implementation provides much better generalisation performance with fewer network parameters, compared to the Gaussian RBF networks. | - |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | IEE-INST ELEC ENG | - |
dc.title | Evolving mixture of experts for nonlinear time series modelling and prediction | - |
dc.type | Article | - |
dc.identifier.wosid | 000173508400023 | - |
dc.identifier.scopusid | 2-s2.0-0037012114 | - |
dc.type.rims | ART | - |
dc.citation.volume | 38 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 34 | - |
dc.citation.endingpage | 35 | - |
dc.citation.publicationname | ELECTRONICS LETTERS | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Lee, Ju-Jang | - |
dc.contributor.nonIdAuthor | Hong, SG | - |
dc.contributor.nonIdAuthor | Oh, SK | - |
dc.contributor.nonIdAuthor | Kim, MS | - |
dc.type.journalArticle | Article | - |
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