Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes-Reference-Cited by-同舟云学术

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Fast tunable gradient RBF networks for online modeling of nonlinear and nonstationary dynamic processes

Published:2020-09 Issue: Volume:93 Page:53-65
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Liu Tong,Chen Sheng^ORCID,Liang Shan,Du Dajun,Harris Chris J.

Funder

Chinese Scholarship Council

National Natural Science Foundation of China

Key Research Program of Chongqing Science & Technology Commission

Graduate Scientific Research and Innovation Foundation of Chongqing, China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference41 articles.

1. Big data ortunities and challenges: Discussions from data analytics perspectives;Zhou;IEEE Comput. Intell. Mag.,2014

2. Learning in nonstationary environments: A survey;Ditzler;IEEE Comput. Intell. Mag.,2015

3. Data mining and analytics in the process industry: The role of machine learning;Ge;IEEE Access,2017

4. The state of the art in chemical process control in Japan: Good practice and questionnaire survey;Kano;J. Process Control,2010

5. Adaptive soft sensor development for multi-output industrial processes based on selective ensemble learning;Shao;IEEE Access,2018

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