Convergence analysis of a linearized Rubner network with modified lateral weight behavior
dc.contributor.author | Berzal, José Andrés | |
dc.contributor.author | Zufiria, Pedro | |
dc.date.accessioned | 2024-02-09T11:29:51Z | |
dc.date.available | 2024-02-09T11:29:51Z | |
dc.date.issued | 2006 | |
dc.identifier.isbn | 978-84-611-190 | |
dc.identifier.uri | https://hdl.handle.net/10115/30242 | |
dc.description | In 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.abstract | In 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.language.iso | eng | es |
dc.publisher | Vigo Aguiar, Jesús | es |
dc.subject | Neural Networks | es |
dc.subject | Hebbian and Rubner Neural Networks | es |
dc.subject | Stability analysis | es |
dc.subject | Principal Component Analysis | es |
dc.subject | Artificial Intelligence Algorithms | es |
dc.title | Convergence analysis of a linearized Rubner network with modified lateral weight behavior | es |
dc.type | info:eu-repo/semantics/bookPart | es |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | es |
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