Multilayer reservoir computing to overcome the memory-nonlinearity trade-off

Jaume Suárez, Samuel

dc.contributor	Cornelles Soriano, Miguel
dc.contributor.author	Jaume Suárez, Samuel
dc.date	2019
dc.date.accessioned	2020-12-17T11:11:26Z
dc.date.available	2020-12-17T11:11:26Z
dc.date.issued	2019-09-18
dc.identifier.uri	http://hdl.handle.net/11201/154698
dc.description.abstract	[eng] Machine learning, and more precisely reservoir computing, is a state-of-the-art research field because of its successful application to time-dependent computationallyhard tasks. Along this MSc Thesis, we will extend the basic concepts and features of Echo State Networks, which are a specific type of reservoir computers. The main goal of this work will be finding out a reservoir configuration on which the so-called memory-nonlinearity trade-off is overcome. In order to achieve such an enhancement, we will explore changes in the main properties of the reservoir, including its parameters, its degree of linearity and its topology	ca
dc.format	application/pdf
dc.language.iso	eng	ca
dc.publisher	Universitat de les Illes Balears
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	all rights reserved
dc.subject	53 - Física	ca
dc.title	Multilayer reservoir computing to overcome the memory-nonlinearity trade-off	ca
dc.type	info:eu-repo/semantics/masterThesis	ca
dc.type	info:eu-repo/semantics/publishedVersion
dc.date.updated	2019-11-29T10:56:47Z