Convergence analysis of a linearized Rubner network with modified lateral weight behavior

dc.contributor.authorBerzal, José Andrés
dc.contributor.authorZufiria, Pedro
dc.date.accessioned2024-02-09T11:29:51Z
dc.date.available2024-02-09T11:29:51Z
dc.date.issued2006
dc.descriptionIn this work a novel Rubner-type neural network architecture is presented. The proposed modifications on the antihebbian connections and learning laws, lead to a partially decoupled structure whose stability analysis can be performed without the need of a time-scale hypothesis.es
dc.description.abstractIn this work a novel hebbian neural network architecture for Principal Component Analysis is presented. The proposed network is obtained via a linearization and modification of the standard Rubner model. The new antihebbian connections and learning laws define a partially decoupled net structure. This specific connectivity among the neurons allows for a stability analysis of the whole network where there is no need to assume a priori a time-scale hypothesis between the neurons dynamics.es
dc.identifier.isbn978-84-611-190
dc.identifier.urihttps://hdl.handle.net/10115/30242
dc.language.isoenges
dc.publisherVigo Aguiar, Jesúses
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses
dc.subjectNeural Networkses
dc.subjectHebbian and Rubner Neural Networkses
dc.subjectStability analysises
dc.subjectPrincipal Component Analysises
dc.subjectArtificial Intelligence Algorithmses
dc.titleConvergence analysis of a linearized Rubner network with modified lateral weight behaviores
dc.typeinfo:eu-repo/semantics/bookPartes

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