Data-Driven Prediction of Sintering Burn-Through Point Based on Novel Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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