Adaptive Nonlinear System Identification with Echo State Networks

Echo state networks (ESN) are a novel approach to recurrent neural networktraining. 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 outputweights becomes a linear, uniquely solvable task of MSE minimization. This article reviews the basic ideas and describes anonline adaptation scheme based on the RLS algorithm known from adaptive linear systems. As an example, a 10th order NARMAsystem is adaptively identified.