Abstract
Echo state networks (ESN) are a novel approach to recurrent neural
network training. An ESN consists of a large, fixed, recurrent
"reservoir" network, from which the desired output is
obtained by training suitable output connection weights.
Determination of optimal output weights becomes a linear, uniquely
solvable task of MSE minimization. This article reviews the basic
ideas and describes an online adaptation scheme based on the RLS
algorithm known from adaptive linear systems. As an example, a 10-th
order NARMA system is adaptively identified. The known benefits of
the RLS algorithms carry over from linear systems to nonlinear ones;
specifically, the convergence rate and misadjustment can be
determined at design time.
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