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dc.contributor.authorVelasco García, Manel
dc.contributor.authorMartín Rull, Enric Xavier
dc.contributor.authorAngulo Bahón, Cecilio
dc.contributor.authorMartí Colom, Pau
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2014-11-06T13:02:53Z
dc.date.created2014-05
dc.date.issued2014-05
dc.identifier.citationVelasco, M. [et al.]. LTI ODE-valued neural networks. "Applied intelligence", Maig 2014, vol. 41, núm. 2, p. 594-605.
dc.identifier.issn0924-669X
dc.identifier.urihttp://hdl.handle.net/2117/24573
dc.description.abstractA dynamical version of the classical McCulloch & Pitts’ neural model is introduced in this paper. In this new approach, artificial neurons are characterized by: i) inputs in the form of differentiable continuous-time signals, ii) linear time-invariant ordinary differential equations (LTI ODE) for connection weights, and iii) activation functions evaluated in the frequency domain. It will be shown that this new characterization of the constitutive nodes in an artificial neural network, namely LTI ODE-valued neural network (LTI ODEVNN), allows solving multiple problems at the same time using a single neural structure. Moreover, it is demonstrated that LTI ODEVNNs can be interpreted as complex-valued neural networks (CVNNs). Hence, research on this topic can be applied in a straightforward form. Standard boolean functions are implemented to illustrate the operation of LTI ODEVNNs. Concluding the paper, several future research lines are highlighted, including the need for developing learning algorithms for the newly introduced LTI ODEVNNs.
dc.format.extent12 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherDynamical neural network
dc.subject.otherParallel problem solving
dc.subject.otherComplex-valued neural network
dc.titleLTI ODE-valued neural networks
dc.typeArticle
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. GR-DCS - Sistemes Distribuïts de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes
dc.contributor.groupUniversitat Politècnica de Catalunya. GREC - Grup de Recerca en Enginyeria del Coneixement
dc.identifier.doi10.1007/s10489-014-0548-7
dc.relation.publisherversionhttp://link.springer.com/article/10.1007/s10489-014-0548-7
dc.rights.accessRestricted access - publisher's policy
drac.iddocument15159752
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorVelasco, M.; Martin, E.X.; Angulo, C.; Marti, P.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameApplied intelligence
upcommons.citation.volume41
upcommons.citation.number2
upcommons.citation.startingPage594
upcommons.citation.endingPage605


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