Dynamic evolutionary model based on a multi-sampling inherited HAPFNN for an aluminium electrolysis manufacturing system-Reference-Cited by-同舟云学术

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Dynamic evolutionary model based on a multi-sampling inherited HAPFNN for an aluminium electrolysis manufacturing system

Published:2021-02 Issue: Volume:99 Page:106925
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Ding Wei,Yao Lizhong,Li Yanyan,Long Wei,Yi Jun,He Tiantian^ORCID

Funder

National Natural Science Foundation of China

Chongqing Research Program of Basic Research and Frontier Technology, China

Institutions of Higher Education in Chongqing, China

China Scholarship Council

Publisher

Elsevier BV

Subject

Software

Reference52 articles.

1. Multimode process monitoring based on robust dictionary learning with application to aluminium electrolysis process;Yang;Neurocomputing,2019

2. A review of intelligent optimal manufacturing for aluminum reduction production;Gui;Zidonghua Xuebao/Acta Autom. Sin.,2018

3. Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization : application to thermal enhanced oil recovery processes;Menad;Fuel,2019

4. Improved heat transfer modeling of the top of aluminum electrolysis cells;Allard;Int. J. Heat Mass Transfer,2019

5. Device used for handling equipment of electrolysis cell to produce aluminum by igneous electrolysis;A,2016

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3. An innovative model of personalized product service system (PPSS) for open community collaborative supply (OCCS): Selection, configuration, and optimization;Journal of Cleaner Production;2024-01

4. Piggybacking on past problem for faster optimization in aluminum electrolysis process design;Engineering Applications of Artificial Intelligence;2023-11

5. A dynamic spatial distributed information clustering method for aluminum electrolysis cell;Engineering Applications of Artificial Intelligence;2023-11

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