Multiple-variance identification methods are based on the use of input signals with different powers for nonlinear system identification. They overcome the problem of the locality of the solution of traditional identification methods that well approximates the system only for inputs with approximately the same power of the identification signal. In this context, it is possible to further improve the nonlinear filter estimation exploiting as input signals the perfect periodic sequences that guarantee the orthogonality of the Wiener basis functions used for identification. Experimental results involving real measurements show that the proposed approach can accurately model nonlinear devices on a wide range of input variances. This property is particularly useful when modeling systems with high dynamic inputs, like audio amplifiers.
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