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Chasing the Echo State Property · Around

2018

Chasing the Echo State Property

Gallicchio, Claudio

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Reservoir Computing (RC) provides an efficient way for designing dynamical recurrent neural models.

While training is restricted to a simple output component, the recurrent connections are left untrained after initialization, subject to stability constraints specified by the Echo State Property (ESP).
Literature conditions for the ESP typically fail to properly account for the effects of driving input signals, often limiting the potentialities of the RC approach.
In this paper, we study the fundamental aspect of asymptotic stability of RC models in presence of driving input, introducing an empirical ESP index that enables to easily analyze the stability regimes of reservoirs.

Built on

The ”echo state” approach to analysing and training recurrent neural networks - with an erratum note
H. Jaeger · 2001
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Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
H. Jaeger and H. Haas · 2004
Earlier work this paper cites.
A tighter bound for the echo state property
M. Buehner and P. Young · 2006
Earlier work this paper cites.

Similar

Reservoir computing approaches to recurrent neural network training
M. Lukoševičius and H. Jaeger · 2009
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Architectural and Markovian factors of Echo State Networks
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Re-visiting the echo state property
I.B. Yildiz, H. Jaeger, and S.J. Kiebel · 2012
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Then

Echo state property linked to an input: Exploring a fundamental characteristic of recurrent neural networks
G. Manjunath and H. Jaeger · 2013
Later among the works it cites.
OCReP: An Optimally Conditioned Regularization for pseudoinversion based neural training
R. Cancelliere, M. Gai, P. Gallinari, and L. Rubini · 2015
Later among the works it cites.

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