The multiple-variance method is a cross-correlation method that exploits input signals with different powers for the identification of a nonlinear system by means of the Volterra series. It overcomes the problem of the locality of the solution of traditional nonlinear identification methods that successfully approximate only for inputs having approximately the same power of the identification signal. The multiple-variance method improves the model performance in case of inputs with high dynamic range. This method is used to identify three different tube amplifiers, and it is applied to a novel reduced Volterra model. This overcomes the problem of the very large number of coefficients required by the Volterra series, the so-called “course of dimensionality.” The paper demonstrates the effectiveness of the multiple-variance methodology in terms of system identification error and computational complexity.
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