Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote

Variable Selection ◽

Selection Method ◽

Soft Sensor ◽

Artificial Neural ◽

Nonnegative Garrote

Performance of smoothly clipped absolute deviation as a variable selection method in the artificial neural network‐based QSAR studies

Journal of Chemometrics ◽

10.1002/cem.3338 ◽

2021 ◽

Author(s):

Zeinab Mozafari ◽

Mansour Arab Chamjangali ◽

Mohammad Arashi ◽

Nasser Goudarzi

Keyword(s):

Neural Network ◽

Variable Selection ◽

Selection Method ◽

Absolute Deviation ◽

Qsar Studies ◽

Smoothly Clipped Absolute Deviation ◽

Computer Aided Chemical Engineering - 24th European Symposium on Computer Aided Process Engineering ◽

Soft Sensor Development with Nonlinear Variable Selection Using Nonnegative Garrote and Artificial Neural Network

10.1016/b978-0-444-63456-6.50148-4 ◽

2014 ◽

pp. 883-888 ◽

Cited By ~ 1

Author(s):

Kai Sun ◽

JiaLin Liu ◽

Jia-Lin Kang ◽

Shi-Shang Jang ◽

David Shan-Hill Wong ◽

...

Keyword(s):

Neural Network ◽

Variable Selection ◽

Soft Sensor ◽

Sensor Development ◽

Artificial Neural ◽

Nonnegative Garrote

An Input Variable Selection Method for the Artificial Neural Network of Shear Stiffness of Worsted Fabrics

Statistical Analysis and Data Mining The ASA Data Science Journal ◽

10.1002/sam.10020 ◽

2009 ◽

Vol 1 (5) ◽

pp. 287-295 ◽

Cited By ~ 2

Author(s):

Ting Chen ◽

Chong Zhang ◽

Xia Chen ◽

Liqing Li

Keyword(s):

Neural Network ◽

Variable Selection ◽

Shear Stiffness ◽

Selection Method ◽

2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) ◽

A fast variable selection for nonnegative garrote-based artificial neural network

10.1109/ecticon.2016.7561438 ◽

2016 ◽

Author(s):

Chaiyasit Waleesuksan ◽

Sarawan Wongsa

Keyword(s):

Neural Network ◽

Variable Selection ◽

Fast Variable ◽

Selection For ◽

Artificial Neural ◽

Nonnegative Garrote

Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor

Journal of Microbiological Methods ◽

10.1016/j.mimet.2019.02.002 ◽

2019 ◽

Vol 159 ◽

pp. 5-11 ◽

Cited By ~ 8

Author(s):

Chitra Murugan ◽

Pappa Natarajan

Keyword(s):

Neural Network ◽

Fungal Biomass ◽

Soft Sensor ◽

Voltage stability monitoring by artificial neural network using a regression-based feature selection method

Expert Systems with Applications ◽

10.1016/j.eswa.2007.08.059 ◽

2008 ◽

Vol 35 (4) ◽

pp. 1802-1808 ◽

Cited By ~ 19

Author(s):

S. Chakrabarti

Keyword(s):

Neural Network ◽

Feature Selection ◽

Voltage Stability ◽

Feature Selection Method ◽

Selection Method ◽

Stability Monitoring ◽

Design and Application of a Variable Selection Method for Multilayer Perceptron Neural Network With LASSO

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2016.2542866 ◽

2017 ◽

Vol 28 (6) ◽

pp. 1386-1396 ◽

Cited By ~ 27

Author(s):

Kai Sun ◽

Shao-Hsuan Huang ◽

David Shan-Hill Wong ◽

Shi-Shang Jang

Keyword(s):

Neural Network ◽

Variable Selection ◽

Multilayer Perceptron ◽

Selection Method ◽

Design And Application

Predicting water quality during urbanization based on a causality-based input variable selection method modified back-propagation neural network

Environmental Science and Pollution Research ◽

10.1007/s11356-020-10514-8 ◽

2020 ◽

Vol 28 (1) ◽

pp. 960-973

Author(s):

Xinzi Wang ◽

Kejia Wang ◽

Jiamu Ding ◽

Xinqi Chen ◽

Yi Li ◽

...

Keyword(s):

Neural Network ◽

Water Quality ◽

Variable Selection ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Selection Method ◽

Variable Selection Method

2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) ◽

A New Variable Selection Method for Soft Sensor Based on Deep Learning

10.1109/ccis.2018.8691405 ◽

2018 ◽

Author(s):

Xiao Wang

Keyword(s):

Deep Learning ◽

Variable Selection ◽

Selection Method ◽

Soft Sensor ◽

Variable Selection Method

A Variable Selection Method for Improving Variable Selection Consistency and Soft Sensor Performance*

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147774 ◽

2020 ◽

Author(s):

Jangwon Lee ◽

Jesus Flores-Cerrillo ◽

Jin Wang ◽

Q. Peter He

Keyword(s):

Variable Selection ◽

Selection Method ◽

Soft Sensor ◽

Sensor Performance ◽

Selection Consistency