Nonlinearity recovery by standard and aggregative orthogonal series algorithms

Przemysław Śliwiński, Paweł Wachel and Szymon Łagosz

Applied Stochastic Models in Business and Industry, 2018, vol. 34, issue 5, 659-666

Abstract: In this paper, the problem of nonlinearity recovery in Hammerstein systems is considered. Two algorithms are presented: the first is a standard orthogonal series algorithm, whereas the other, ie, the aggregative one, exploits the convex programming approach. The finite sample size properties of both approaches are examined, compared, and illustrated in a numerical experiment. The aggregative algorithm performs better when the number of measurements is comparable to the number of parameters; however, it also imposes additional smoothness restrictions on the recovered nonlinearities.

Date: 2018
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https://doi.org/10.1002/asmb.2311

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:34:y:2018:i:5:p:659-666

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