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Article overview
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Forecasting, filtering, and reconstruction of stochastic stationary signals using discrete-time reservoir computers | Lyudmila Grigoryeva
; Julie Henriques
; Juan-Pablo Ortega
; | Date: |
1 Aug 2015 | Abstract: | This paper extends the notion of information processing capacity for
{non-independent } input signals in the context of reservoir computing (RC).
The presence of input autocorrelation makes worthwhile the treatment of
forecasting and filtering problems for which we explicitly compute this
generalized capacity as a function of the reservoir parameter values using a
streamlined model. The reservoir model leading to these developments is used to
show that, whenever that approximation is valid, this computational paradigm
satisfies the so called separation and fading memory properties that are
usually associated with good information processing performances. We show that
several standard memory, forecasting, and filtering problems that appear in the
parametric stochastic time series context can be readily formulated and tackled
via RC which, as we show, significantly outperforms standard techniques in some
instances. | Source: | arXiv, 1508.0144 | Services: | Forum | Review | PDF | Favorites |
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