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LTI ODE-valued neural networks adaptation of the back propagation algorithm
dc.contributor | Angulo Bahón, Cecilio |
dc.contributor | Velasco García, Manel |
dc.contributor.author | Prat Baucells, Albert |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2019-03-27T14:51:45Z |
dc.date.available | 2019-03-27T14:51:45Z |
dc.date.issued | 2018-06-21 |
dc.identifier.uri | http://hdl.handle.net/2117/130951 |
dc.description.abstract | In [Velasco et al., 2014], a new approach of the classical artificial neural network archi-tecture is introduced, named ’LTI ODE-valued neural networks’, whereLTI ODEstandsfor Linear Time Invariant Ordinal Differential Equation. In this novel system, nodes inthe artificial neural network are characterized by: inputs in the form of differentiablecontinuous-time signals; linear time-invariant ordinary differential equations (LTI ODE)as connection weights; and activation functions evaluated in the frequency domain.It was shown that this new configuration allows solving multiple problems at the sametime using a common neural structure. However, the article concludes with the need fordeveloping learning algorithms for the new model of neural network.Taking as starting point the drawback pointed out in [Velasco et al., 2014], the mainobjective of this master thesis is to develop a training algorithm for a LTI ODE-valuedneural network. As a first and natural approach, modifications of the BackPropagationalgorithm is considered as a general framework. Moreover, since the nature of the inputsare differentiable continuous-time signals, it is analyzed how to obtain a model that canbe physically implemented in the form of an analogical circuit |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Algorithms |
dc.title | LTI ODE-valued neural networks adaptation of the back propagation algorithm |
dc.type | Master thesis |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Algorismes |
dc.identifier.slug | ETSEIB-240.136127 |
dc.rights.access | Open Access |
dc.date.updated | 2018-07-17T05:24:50Z |
dc.audience.educationlevel | Màster |
dc.audience.mediator | Escola Tècnica Superior d'Enginyeria Industrial de Barcelona |
dc.audience.degree | MÀSTER UNIVERSITARI EN ENGINYERIA INDUSTRIAL (Pla 2014) |