An Experimental Analysis of Reservoir Parameters of the Echo State Queueing Network Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86088101" target="_blank" >RIV/61989100:27240/13:86088101 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/13:86088101

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-01781-5_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-01781-5_2" target="_blank" >10.1007/978-3-319-01781-5_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Experimental Analysis of Reservoir Parameters of the Echo State Queueing Network Model

Popis výsledku v původním jazyce

During the last years, there has been a growing interest in the Reservoir Computing (RC) paradigm. Recently, a new RC model was presented under the name of Echo State Queueing Networks (ESQN). This model merges ideas from Queueing Theory and one of the two pioneering RC techniques, Echo State Networks. In a RC model there is a dynamical system called reservoir which serves to expand the input data into a larger space. This expansion can enhance the linear separability of the data. In the case of ESQN, the reservoir is a Recurrent Neural Network composed of spiking neurons which fire positive and negative signals. Unlike other RC models, an analysis of the dynamics behavior of the ESQN system is still to be done. In this work, we present an experimentalanalysis of these dynamics. In particular, we study the impact of the spectral radius of the reservoir in system stability. In our experiments, we use a range of benchmark time series data.

Název v anglickém jazyce

An Experimental Analysis of Reservoir Parameters of the Echo State Queueing Network Model

Popis výsledku anglicky

During the last years, there has been a growing interest in the Reservoir Computing (RC) paradigm. Recently, a new RC model was presented under the name of Echo State Queueing Networks (ESQN). This model merges ideas from Queueing Theory and one of the two pioneering RC techniques, Echo State Networks. In a RC model there is a dynamical system called reservoir which serves to expand the input data into a larger space. This expansion can enhance the linear separability of the data. In the case of ESQN, the reservoir is a Recurrent Neural Network composed of spiking neurons which fire positive and negative signals. Unlike other RC models, an analysis of the dynamics behavior of the ESQN system is still to be done. In this work, we present an experimentalanalysis of these dynamics. In particular, we study the impact of the spectral radius of the reservoir in system stability. In our experiments, we use a range of benchmark time series data.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0055" target="_blank" >EE2.3.30.0055: Nové kreativní týmy v prioritách vědeckého bádání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 237

ISBN

978-3-319-01780-8

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

10

Strana od-do

13-22

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Ostrava

Datum konání akce

22. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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