首页|Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative
Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative
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Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative
The variable selection of high dimensional nonparametric nonlinear systems aims to select the contributing variables or to eliminate the redundant variables.For a high dimensional nonparametric nonlinear system,however,identifying whether a vari-able contributes or not is not easy.Therefore,based on the Fourier spectrum of density-weighted derivative,one novel variable selection approach is developed,which does not suffer from the dimensionality curse and improves the identification accuracy.Further-more,a necessary and sufficient condition for testing a variable whether it contributes or not is provided.The proposed approach does not require strong assumptions on the distribution,such as elliptical distribution.The simulation study verifies the effectiveness of the novel variable selection algorithm.
nonlinear system identificationvariable selectionFourier spectrumnon-parametric nonlinear system
Bing SUN、Changming CHENG、Qiaoyan CAI、Zhike PENG
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State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240,China
China Academy of Launch Vehicle Technology,Beijing 100076,China
School of Mechanical Engineering,Ningxia University,Yinchuan 750021,China
nonlinear system identification variable selection Fourier spectrum non-parametric nonlinear system
NETL
NSTL
万方数据
维普
