Publikation: Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
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Datum
2018
Autor:innen
Ortega, Juan-Pablo
Herausgeber:innen
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Open Access Hybrid
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Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
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Published
Erschienen in
Journal of Machine Learning Research : JMLR. 2018, 19, 24. ISSN 1532-4435. eISSN 1533-7928
Zusammenfassung
A new class of non-homogeneous state-affine systems is introduced for use in reservoir computing. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts are causal, time-invariant, and satisfy the fading memory property and second, that a subset of this class is universal in the category of fading memory filters with stochastic almost surely uniformly bounded inputs. This means that any discrete-time filter that satisfies the fading memory property with random inputs of that type can be uniformly approximated by elements in the non-homogeneous state-affine family.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
510 Mathematik
Schlagwörter
reservoir computing, universality, state-affine systems, SAS, echo state networks, ESN, echo state affine systems, machine learning, fading memory property, linear training, stochastic signal treatment
Konferenz
Rezension
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ISO 690
GRIGORYEVA, Lyudmila, Juan-Pablo ORTEGA, 2018. Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems. In: Journal of Machine Learning Research : JMLR. 2018, 19, 24. ISSN 1532-4435. eISSN 1533-7928BibTex
@article{Grigoryeva2018Unive-43336,
year={2018},
title={Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems},
url={http://jmlr.org/papers/v19/18-020.html},
volume={19},
issn={1532-4435},
journal={Journal of Machine Learning Research : JMLR},
author={Grigoryeva, Lyudmila and Ortega, Juan-Pablo},
note={Article Number: 24}
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<dcterms:abstract xml:lang="eng">A new class of non-homogeneous state-affine systems is introduced for use in reservoir computing. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts are causal, time-invariant, and satisfy the fading memory property and second, that a subset of this class is universal in the category of fading memory filters with stochastic almost surely uniformly bounded inputs. This means that any discrete-time filter that satisfies the fading memory property with random inputs of that type can be uniformly approximated by elements in the non-homogeneous state-affine family.</dcterms:abstract>
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URL der Originalveröffentl.
Prüfdatum der URL
2018-09-20
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Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
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Ja
