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• W2964191572 abstract "The object of this paper is the identification of Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of p basis functions. We model the system dynamics by means of an np-dimensional vector. This vector, usually referred to as overparameterized vector, contains all the combinations between the nonlinearity coefficients and the first n samples of the impulse response of the linear block. The estimation of the overparameterized vector is performed with a new regularized kernel-based approach. To this end, we introduce a novel kernel tailored for overparameterized models, which yields estimates that can be uniquely decomposed as the combination of an impulse response and p coefficients of the static nonlinearity. As part of the work, we establish a clear connection between the proposed identification scheme and our recently developed nonparametric method based on the stable spline kernel." @default.
• W2964191572 created "2019-07-30" @default.
• W2964191572 creator A5000544193 @default.
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• W2964191572 date "2016-01-01" @default.
• W2964191572 modified "2023-10-01" @default.
• W2964191572 title "A kernel-based approach to overparameterized Hammerstein system identification" @default.
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• W2964191572 hasPublicationYear "2016" @default.
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