Reservoir computing based on delay-dynamical systems

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dc.contributor Danckaert, Jan
dc.contributor Departament de Física
dc.creator Appeltant, Lennert 2012 2017-07-10T09:36:19Z 2017-07-10T09:36:19Z
dc.description [eng] Today, except for mathematical operations, our brain functions much faster and more efficient than any supercomputer. It is precisely this form of information processing in neural networks that inspires researchers to create systems that mimic the brain’s information processing capabilities. In this thesis we propose a novel approach to implement these alternative computer architectures, based on delayed feedback. We show that one single nonlinear node with delayed feedback can replace a large network of nonlinear nodes. First we numerically investigate the architecture and performance of delayed feedback systems as information processing units. Then we elaborate on electronic and opto-electronic implementations of the concept. Next to evaluating their performance for standard benchmarks, we also study task independent properties of the system, extracting information on how to further improve the initial scheme. Finally, some simple modifications are suggested, yielding improvements in terms of speed or performance
dc.format application/pdf
dc.language eng
dc.publisher Universitat de les Illes Balears
dc.relation Tesis doctorals de la UIB
dc.rights all rights reserved
dc.rights info:eu-repo/semantics/openAccess
dc.subject Reservoir computing, delayed feedback systems, machine learning
dc.title Reservoir computing based on delay-dynamical systems
dc.type info:eu-repo/semantics/doctoralThesis
dc.type info:eu-repo/semantics/publishedVersion
dc.doctorat Doctorat en Física (extingit)

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