Temat: Nonparametric regression

Skocz do pozycji: 1.

Tytuł:: Generalized kernel regression estimate for the identification of Hammerstein systems
Autorzy:: Mzyk, G.
Tematy:: system Hammersteina
regresja nieparametryczna
estymacja jądra
Hammerstein system
nonparametric regression
kernel estimation; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/929610.pdf Link otwiera się w nowym oknie
Opis:: A modified version of the classical kernel nonparametric identification algorithm for nonlinearity recovering in a Hammerstein system under the existence of random noise is proposed. The assumptions imposed on the unknown characteristic are weak. The generalized kernel method proposed in the paper provides more accurate results in comparison with the classical kernel nonparametric estimate, regardless of the number of measurements. The convergence in probability of the proposed estimate to the unknown characteristic is proved and the question of the convergence rate is discussed. Illustrative simulation examples are included.
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Bandwidth selection for kernel generalized regression neural networks in identification of hammerstein systems
Autorzy:: Lv, Jiaqing
Pawlak, Mirosław
Tematy:: generalized regression neural network
nonparametric estimation
bandwidth
data-driven selection
nonlinear system
Hammerstein system; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/2031118.pdf Link otwiera się w nowym oknie
Opis:: This paper addresses the issue of data-driven smoothing parameter (bandwidth) selection in the context of nonparametric system identification of dynamic systems. In particular, we examine the identification problem of the block-oriented Hammerstein cascade system. A class of kernel-type Generalized Regression Neural Networks (GRNN) is employed as the identification algorithm. The statistical accuracy of the kernel GRNN estimate is critically influenced by the choice of the bandwidth. Given the need of data-driven bandwidth specification we propose several automatic selection methods that are compared by means of simulation studies. Our experiments reveal that the method referred to as the partitioned cross-validation algorithm can be recommended as the practical procedure for the bandwidth choice for the kernel GRNN estimate in terms of its statistical accuracy and implementation aspects.
Dostawca treści:: Biblioteka Nauki

Artykuł

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