Feasibility of random basis function approximators for modeling and control

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dc.creatorTyukin, Ivan
dc.creatorProkhorov, Danil
dc.date2009-05-05
dc.date.accessioned2026-07-07T13:11:56Z
dc.date.available2026-07-07T13:11:56Z
dc.descriptionWe discuss the role of random basis function approximators in modeling and control. We analyze the published work on random basis function approximators and demonstrate that their favorable error rate of convergence O(1/n) is guaranteed only with very substantial computational resources. We also discuss implications of our analysis for applications of neural networks in modeling and control.
dc.identifierhttps://arxiv.org/abs/0905.0677
dc.identifierhttp://arxiv.org/abs/0905.0677
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/229441
dc.subjectNeural and Evolutionary Computing
dc.subjectArtificial Intelligence
dc.titleFeasibility of random basis function approximators for modeling and control
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