Training feedforward neural networks with dynamic particle swarm optimisation

dc.contributor.authorRakitianskaia, A.S. (Anastassia Sergeevna)
dc.contributor.authorEngelbrecht, Andries P.
dc.contributor.emailengel@cs.up.ac.zaen_US
dc.date.accessioned2012-12-11T11:53:23Z
dc.date.available2012-12-11T11:53:23Z
dc.date.issued2012-09
dc.description.abstractParticle swarm optimisation has been successfully applied to train feedforward neural networks in static environments.Many real-world problems to which neural networks are applied are dynamic in the sense that the underlying data distribution changes over time. In the context of classification problems, this leads to concept drift where decision boundaries may change over time. This article investigates the applicability of dynamic particle swarm optimisation algorithms as neural network training algorithms under the presence of concept drift.en_US
dc.description.urihttp://link.springer.com/journal/11721en_US
dc.identifier.citationRakitianskaia, AS & Engelbrecht, AP 2012, 'Training feedforward neural networks with dynamic particle swarm optimisation', Swarm Intelligence, vol. 6, no. 3, pp. 233-270, doi: 10.1007/s11721-012-0071-6en_US
dc.identifier.issn1935-3812 (print)
dc.identifier.issn1935-3820 (online)
dc.identifier.other10.1007/s11721-012-0071-6
dc.identifier.urihttp://hdl.handle.net/2263/20669
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© Springer Science + Business Media, LLC 2012. The original publication is available at www.springerlink.comen_US
dc.subjectSwarm intelligenceen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.subjectNeural networksen_US
dc.subjectDynamic environmentsen_US
dc.subjectClassificationen_US
dc.subjectConcept driften_US
dc.titleTraining feedforward neural networks with dynamic particle swarm optimisationen_US
dc.typePostprint Articleen_US

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