Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality-Reference-Cited by-同舟云学术

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Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality

Published:2019-03 Issue: Volume:101 Page:138-149
ISSN:0005-1098
Container-title:Automatica
language:en
Short-container-title:Automatica

Author:

Bai Er-Wei,Cheng Changming,Zhao Wen-Xiao

Funder

National Science Foundation

National Key Research and Development Program of China

National Key Basic Research Program of China

National Natural Science Foundation (NSF) of ChinaNational Natural Science Foundation (NSF) of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Reference31 articles.

1. Non-parametric nonlinear system identification: An asymptotic minimum mean squared error estimator;Bai;IEEE Transactions on Automatic Control,2010

2. Identification of an additive nonlinear system and its applications in generalized Hammerstein models;Bai;Automatica,2008

3. Kernel based approaches to local nonlinear nonparametric variable selection;Bai;Automatica,2014

4. Recursive direct weight optimization in nonlinear system identification: a minimal probability approach;Bai;IEEE Transactions on Automatic Control,2007

5. Selection of variables and dimension reduction in high-dimensional non-parametric regression;Bertin;Electronic Journal of Statistics,2008

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