Data-Driven Prediction of Sintering Burn-Through Point Based on Novel Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Shang20101,
-
author = "Xiu-qin Shang and Jian-gang Lu and You-xian Sun and
Jun Liu and Yu-qian Ying",
-
title = "Data-Driven Prediction of Sintering Burn-Through Point
Based on Novel Genetic Programming",
-
journal = "Journal of Iron and Steel Research, International",
-
volume = "17",
-
number = "12",
-
pages = "1--5",
-
year = "2010",
-
ISSN = "1006-706X",
-
DOI = "doi:10.1016/S1006-706X(10)60188-4",
-
URL = "http://www.sciencedirect.com/science/article/B82XP-51VB3C8-1/2/1de00900cc3d2e7a67b60aa929329773",
-
keywords = "genetic algorithms, genetic programming, burn-through
point, K-means clustering",
-
abstract = "An empirical dynamic model of burn-through point (BTP)
in sintering process was developed. The K-means
clustering was used to feed distribution according to
the cold bed permeability, which was estimated by the
superficial gas velocity in the cold stage. For each
clustering, a novel genetic programming (NGP) was
proposed to construct the empirical model of the waste
gas temperature and the bed pressure drop in the
sintering stage. The least square method (LSM) and
M-estimator were adopted in NGP to improve the ability
to compute and resist disturbance. Simulation results
show the superiority of the proposed method.",
- }
Genetic Programming entries for
Xiu-qin Shang
Jiangang Lu
Youxian Sun
Jun Liu
Yu-qian Ying
Citations