Evolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Network

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dc.contributor.authorSergeev, S. A.en
dc.contributor.authorMahotilo, K. V.en
dc.date.accessioned2017-07-07T06:04:04Z
dc.date.available2017-07-07T06:04:04Z
dc.date.issued1996
dc.description.abstractThe combination of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs) has already resulted in researchers advancing in quite a few real world applications but it is in control that this alliance yields much appreciable benefit. The paper reports a Radial Basis Function (RBF) network training technique which joins together global strategy of GAs and a local adjusting procedure typical for RBF networks. While activation function window centres and widths are processed via a "slow" numeric GA, output-layer neurone synaptic weights are defined by a "fast" analytical method. The technique allows to minimize not only the network hidden-layer size but also the pattern set required for training the adequate dynamical object neuroemulator.en
dc.identifier.citationSergeev S. A. Evolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Network / S. A. Sergeev, K. V. Mahotilo // Procs of 1st Workshop on Soft Computing WSC-1,1996, August 19-30, On the Internet, Served by Nagoya University, pp. 31-36.en
dc.identifier.orcidhttps://orcid.org/0000-0001-7081-071X
dc.identifier.urihttps://repository.kpi.kharkov.ua/handle/KhPI-Press/30585
dc.language.isoen
dc.publisherNagoya Universityen
dc.subjectgenetic algorithmsen
dc.subjectneural networksen
dc.subjectRBFen
dc.subjectmodellingen
dc.titleEvolutionary Synthesis of Dynamical Object Emulator Based on RBF Neural Networken
dc.typeArticleen

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