Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace-Reference-Cited by-同舟云学术

Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace

Published:2013-12-06 Issue:2 Volume:41 Page:87-98
ISSN:0301-9233
Container-title:Ironmaking & Steelmaking
language:en
Short-container-title:Ironmaking & Steelmaking

Author:

Sanz-García A.,Fernández-Ceniceros J.,Fernández-Martínez R.,Martínez-de-Pisón F. J.

Publisher

Informa UK Limited

Subject

Materials Chemistry,Metals and Alloys,Mechanical Engineering,Mechanics of Materials

Link

http://www.tandfonline.com/doi/pdf/10.1179/1743281212Y.0000000094

Reference28 articles.

1. Model-based control of strip temperature for the heating furnace in continuous annealing

2. Kim J.S. and Chung J.H. ‘Galvannealing behaviour of high strength galvanized sheet steels’, Proc. Galvanised Steel Sheet Forum-Automotive, London, UK, May 2000, IOM Communication Ltd. 103–108.

3. Mining association rules from time series to explain failures in a hot-dip galvanizing steel line

4. Mahieu J., De-Meyer M. and De-Cooman B. C. ‘Galvanizability of high strength steels for automotive applications’, Proc. Galvanised Steel Sheet Forum-Automotive, London, UK, May 2000, IOM Communication Ltd. 185–198.

5. Modeling and Control of an Industrial Continuous Furnace

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Parsimonious fuzzy time series modelling;Expert Systems with Applications;2020-10

2. The consequences of eliminating the soaking section of a commercial hot-dip galvanising line for HSLA production and a control strategy for improving product consistency;Ironmaking & Steelmaking;2019-05-09

3. Development and application of a neural network based coating weight control system for a hot-dip galvanizing line;Frontiers of Information Technology & Electronic Engineering;2018-07

4. Evaluation of a novel GA-based methodology for model structure selection: The GA-PARSIMONY;Neurocomputing;2018-01

5. Improving hotel room demand forecasting with a hybrid GA-SVR methodology based on skewed data transformation, feature selection and parsimony tuning;Logic Journal of the IGPL;2017-09-06