Autonomous dynamical systems based on hardware implementations of delay-reservoir computers

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dc.contributor Cornelles Soriano, Miguel
dc.contributor Fischer, Ingo Estébanez Santos, Irene 2018 2019-06-19T10:31:57Z 2019-06-19T10:31:57Z 2018-09-24
dc.description.abstract [eng] Reservoir computing (RC) is a machine learning technique allowing for novel approaches to realize trainable autonomous nonlinear oscillators. Here we employ delay-based echo state networks with output feedback, simple yet powerful implementations of neuromorphic systems, to reproduce the dynamical behavior of a Rössler chaotic system. Our hardware implementation relies on a delay-based RC topology, and consists of two main elements: an analog Mackey Glass nonlinearity and a Raspberry Pi board. We demonstrate the capacity of our experiment to generate chaotic time-traces in an autonomous manner, and we prove that noise can play a constructive role in the training process, when realizing nonlinear oscillators based on closed-loop operation. We use phase-space reconstruction of the chaotic attractor and the comparison of the frequency spectra, along with recurrence quantification analysis (RQA), to perform a nonlinear data analysis with the aim of comparing the autonomous operation and the original time-series in more detail. ca
dc.format application/pdf
dc.language.iso eng ca
dc.publisher Universitat de les Illes Balears
dc.rights all rights reserved
dc.rights info:eu-repo/semantics/openAccess
dc.subject 53 - Física ca
dc.title Autonomous dynamical systems based on hardware implementations of delay-reservoir computers ca
dc.type info:eu-repo/semantics/masterThesis ca
dc.type info:eu-repo/semantics/publishedVersion 2018-12-20T09:46:13Z

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