Evolutive Approaches for Variable Selection Using a Non-parametric Noise Estimator

Parallel Architectures and Bioinspired Algorithms - Studies in Computational Intelligence ◽

10.1007/978-3-642-28789-3_11 ◽

2012 ◽

pp. 243-266 ◽

Author(s):

Alberto Guillén ◽

Dušan Sovilj ◽

Mark van Heeswijk ◽

Luis Javier Herrera ◽

Amaury Lendasse ◽

...

Keyword(s):

Variable Selection ◽

Parametric Noise ◽

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

10.1016/j.automatica.2018.11.019 ◽

2019 ◽

Vol 101 ◽

pp. 138-149 ◽

Author(s):

Er-Wei Bai ◽

Changming Cheng ◽

Wen-Xiao Zhao

Keyword(s):

Nonlinear Systems ◽

Variable Selection ◽

Curse Of Dimensionality ◽

High Dimensional ◽

Selection Of ◽

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Variable selection using statistical non-parametric tests for classifying production batches into multiple classes

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2019.103830 ◽

2019 ◽

Vol 193 ◽

pp. 103830

Author(s):

Gilberto Muller Beuren ◽

Michel Jose Anzanello

Keyword(s):

Variable Selection ◽

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Frequency domain subspace system identification using non-parametric noise models

Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) ◽

10.1109/cdc.2001.980486 ◽

2003 ◽

Author(s):

R. Pintelon

Keyword(s):

System Identification ◽

Frequency Domain ◽

Parametric Noise ◽

Noise Models ◽

Subspace System Identification ◽

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Local variable selection and dimension determination of nonlinear non-parametric systems

52nd IEEE Conference on Decision and Control ◽

10.1109/cdc.2013.6760550 ◽

2013 ◽

Author(s):

Er-wei Bai ◽

Kang Li ◽

Wenxiao Zhao ◽

Weiyu Xu

Keyword(s):

Variable Selection ◽

Local Variable ◽

Parametric Systems ◽

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Variable selection for non-parametric quantile regression via smoothing spline analysis of variance

Stat ◽

10.1002/sta4.33 ◽

2013 ◽

Vol 2 (1) ◽

pp. 255-268 ◽

Author(s):

Chen-Yen Lin ◽

Howard Bondell ◽

Hao Helen Zhang ◽

Hui Zou

Keyword(s):

Variable Selection ◽

Quantile Regression ◽

Analysis Of Variance ◽

Smoothing Spline ◽

Selection For ◽

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On the use of parametric and non-parametric noise-models in time- and frequency domain system identification

49th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2010.5717606 ◽

2010 ◽

Author(s):

J. Schoukens ◽

Y. Rolain ◽

R. Pintelon

Keyword(s):

System Identification ◽

Frequency Domain ◽

Parametric Noise ◽

Noise Models ◽

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Frequency-domain subspace system identification using non-parametric noise models

10.1016/s0005-1098(02)00036-5 ◽

2002 ◽

Vol 38 (8) ◽

pp. 1295-1311 ◽

Author(s):

R. Pintelon

Keyword(s):

System Identification ◽

Frequency Domain ◽

Parametric Noise ◽

Noise Models ◽

Subspace System Identification ◽

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On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

IFAC Proceedings Volumes ◽

10.3182/20140824-6-za-1003.00122 ◽

2014 ◽

Vol 47 (3) ◽

pp. 4979-4984 ◽

Author(s):

Er-wei Bai ◽

Kang Li ◽

Wenxiao Zhao

Keyword(s):

Variable Selection ◽

Limited Data ◽

Data Set ◽

Parametric System ◽

Selection Of ◽

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Non-parametric Noise Models extracted from Overlapping Sub-records

2006 IEEE Instrumentation and Measurement Technology Conference Proceedings ◽

10.1109/imtc.2006.328227 ◽

2006 ◽

Author(s):

Kurt Barbe ◽

Johan Schoukens

Keyword(s):

Parametric Noise ◽

Noise Models ◽

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Variable selection in nonlinear non-parametric system\\ identification

Scientia Sinica Mathematica ◽

10.1360/n012016-00002 ◽

2016 ◽

Keyword(s):

System Identification ◽

Variable Selection ◽

Parametric System ◽

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