GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace-Reference-Cited by-同舟云学术

GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace

Published:2015-10 Issue: Volume:35 Page:13-28
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Sanz-Garcia A.,Fernandez-Ceniceros J.,Antonanzas-Torres F.,Pernia-Espinoza A.V.,Martinez-de-Pison F.J.

Funder

Agencia de Desarrollo Económico de La Rioja

Instituto de Estudios Riojanos

Banco Santander

FINSKIN

Academy of Finland

Publisher

Elsevier BV

Subject

Software

Reference42 articles.

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2. Optimising annealing process on hot dip galvanising line based on robust predictive models adjusted with genetic algorithms;Martínez-De-Pisón;Ironmak. Steelmak.,2011

3. Optimum model for predicting temperature settings on hot dip galvanising line;Martí nez-de Pisón;Ironmak. Steelmak.,2010

4. Neural intelligent control for a steel plant;Bloch;IEEE Trans. Neural Netw.,1997

5. Application of neural network to the supervisory control of a reheating furnace in the steel industry;Kim;Control Eng. Pract.,1998

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