On Interference of Signals and Generalization in Feedforward Neural Networks

dc.creatorRataj, Artur
dc.date2003-10-06
dc.date2003-11-04
dc.date.accessioned2026-07-07T03:20:24Z
dc.date.available2026-07-07T03:20:24Z
dc.descriptionThis paper studies how the generalization ability of neurons can be affected by mutual processing of different signals. This study is done on the basis of a feedforward artificial neural network. The mutual processing of signals can possibly be a good model of patterns in a set generalized by a neural network and in effect may improve generalization. In this paper it is discussed that the interference may also cause a highly random generalization. Adaptive activation functions are discussed as a way of reducing that type of generalization. A test of a feedforward neural network is performed that shows the discussed random generalization.
dc.description6 pages, 3 figures. Some changes in text to make it more concise
dc.identifierhttps://arxiv.org/abs/cs/0310009
dc.identifierhttp://arxiv.org/abs/cs/0310009
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31816
dc.subjectNeural and Evolutionary Computing
dc.subjectI.2.6
dc.titleOn Interference of Signals and Generalization in Feedforward Neural Networks
dc.typetext

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