Robust neural networks with random weights based on generalized M-estimation and PLS for imperfect industrial data modeling-Reference-Cited by-同舟云学术

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Robust neural networks with random weights based on generalized M-estimation and PLS for imperfect industrial data modeling

Published:2020-12 Issue: Volume:105 Page:104633
ISSN:0967-0661
Container-title:Control Engineering Practice
language:en
Short-container-title:Control Engineering Practice

Author:

Zhou Ping,Xie Jin,Li Wenpeng,Wang Hong,Chai Tianyou

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference32 articles.

1. Using principal components for estimating logistic regression with high-dimensional multicollinear data;Aguilera;Computational Statistics & Data Analysis,2006

2. A multi-time-scale fusion prediction model for the gas utilization rate in a blast furnace;An;Control Engineering Practice,2019

3. PLS generalized linear regression;Bastien;Computational Statistics & Data Analysis,2005

4. Connectionist inference models;Browne;Neural Networks,2002

5. The challenge of control and optimization theory method for production and manufacturing process optimization control;Chai;Acta Automatica Sinica,2009

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4. Stein-rule M-estimation in sparse partially linear models;Japanese Journal of Statistics and Data Science;2023-12-23

5. Online Sequential Sparse Robust Neural Networks With Random Weights for Imperfect Industrial Streaming Data Modeling;IEEE Transactions on Automation Science and Engineering;2023

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