Stochastic convergence analysis of a two-layer perceptron for a system identification model

The authors analyze the stationary points of a two-layer perceptron which attempts to identify the parameters of a specific nonlinear system. The training sequence is modeled as the binary output of the nonlinear system when the input is composed of an independent sequence of zero mean Gaussian vectors with independent components. The training rule is a modified version of Rosenblatt's algorithm. It is shown that the two-layer perceptron correctly identifies all parameters of the unknown nonlinear system.<
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