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dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorFaris, Hossam
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorAl-Madi, Nailah
dc.date.accessioned2018-07-19T05:09:29Z
dc.date.available2018-07-19T05:09:29Z
dc.date.issued2016
dc.identifier.issn1433-3058en_US
dc.identifier.doi10.1007/s00521-016-2559-2en_US
dc.identifier.urihttp://hdl.handle.net/10072/142997
dc.description.abstractTraining artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherSpringeren_US
dc.relation.ispartofpagefrom1en_US
dc.relation.ispartofpageto25en_US
dc.relation.ispartofjournalNeural Computing and Applicationsen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleTraining radial basis function networks using biogeography-based optimizeren_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.en_US
gro.hasfulltextNo Full Text


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