Iterative parameter identification for Hammerstein systems with ARMA noises by using the filtering identification idea
Saida Bedoui,
Kamel Abderrahim,
Feng Ding
Abstract:SummaryIn practical applications, many processes have nonlinear characteristics that require nonlinear models for accurate description. However, constructing such models and determining their parameters are a challenging task. This article explores filtered identification methods for estimating the parameters of a particular type of nonlinear Hammerstein systems with ARMA noise. An auxiliary model‐based least squares algorithm is developed for such systems based on the auxiliary model identification idea. A hi… Show more
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