dc.contributor |
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.contributor |
Angulo Bahón, Cecilio |
dc.contributor |
Velasco García, Manel |
dc.contributor.author |
Prat Baucells, Albert |
dc.date |
2018-06-21 |
dc.identifier.citation |
ETSEIB-240.136127 |
dc.identifier.uri |
http://hdl.handle.net/2117/130951 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::Informàtica |
dc.subject |
Neural networks (Computer science) |
dc.subject |
Algorithms |
dc.subject |
Xarxes neuronals (Informàtica) |
dc.subject |
Algorismes |
dc.title |
LTI ODE-valued neural networks adaptation of the back propagation algorithm |
dc.type |
info:eu-repo/semantics/masterThesis |
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 |