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.

Authors: Orcioni, Simone; Carini, Alberto; Cecchi, Stefania; Terenzi, Alessandro; Piazza, Francesco
Affiliations: Universita Politecnica delle Marche, Ancona, Italy; University of Urbino Carlo Bo, Urbino, Italy(See document for exact affiliation information.)
AES Convention: 144 (May 2018) Paper Number: 9932
Publication Date: May 14, 2018 Import into BibTeX
Subject: Posters: Applications
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=19449

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