Reservoir Computing Universality With Stochastic Inputs

Item Type Journal paper

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.

Authors Gonon, Lukas & Ortega, Juan-Pablo
Journal or Publication Title IEEE Transactions on Neural Networks and Learning Systems
Language English
Subjects computer science
HSG Classification contribution to scientific community
HSG Profile Area SEPS - Quantitative Economic Methods
Refereed Yes
Date 2019
Volume Forthcoming
Depositing User Prof. Ph.D Juan-Pablo Ortega
Date Deposited 10 Nov 2019 09:57
Last Modified 10 Nov 2019 09:57


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Gonon, Lukas & Ortega, Juan-Pablo (2019) Reservoir Computing Universality With Stochastic Inputs. IEEE Transactions on Neural Networks and Learning Systems, Forthcoming

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