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Reservoir Computing Universality With Stochastic Inputs

Journal
IEEE Transactions on Neural Networks and Learning Systems
Type
journal article
Date Issued
2020
Author(s)
Gonon, Lukas  orcid-logo
Ortega, Juan-Pablo  
DOI
10.1109/TNNLS.2019.2899649
Abstract
The universal approximation properties with respect to Lp-type criteria of three important families of reservoir computers with stochastic discrete-time semi-infinite inputs are shown. First, it is proved that linear reservoir systems with either polynomial or neural network readout maps are universal. More importantly, it is proved that the same property holds for two families with linear readouts, namely, trigonometric state-affine systems and echo state networks, which are the most widely used reservoir systems in applications. The linearity in the readouts is a key feature in supervised machine learning applications. It guarantees that these systems can be used in high-dimensional situations and in the presence of large datasets. The Lp criteria used in this paper allow the formulation of universality results that do not necessarily impose almost sure uniform boundedness in the inputs or the fading memory property in the filter that needs to be approximated.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
Yes
Volume
Forthcoming
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/99263
Subject(s)

computer science

Division(s)

SEPS - School of Econ...

MS - Faculty of Mathe...

Eprints ID
258285
File(s)
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Thumbnail Image

open.access

Name

RC8_IEEE.pdf

Size

352.67 KB

Format

Adobe PDF

Checksum (MD5)

4ed3fa3389388b28cf19e59fb6b56335

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