Prediction of laser cutting heat affected zone by extreme learning machine

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@Article{Anicic:2017:OLE,
  author =       "Obrad Anicic and Srdan Jovic and Hivzo Skrijelj and 
                 Bogdan Nedic",
  title =        "Prediction of laser cutting heat affected zone by
                 extreme learning machine",
  journal =      "Optics and Lasers in Engineering",
  volume =       "88",
  pages =        "1--4",
  year =         "2017",
  ISSN =         "0143-8166",
  DOI =          "doi:10.1016/j.optlaseng.2016.07.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0143816616301385",
  abstract =     "Heat affected zone (HAZ) of the laser cutting process
                 may be developed based on combination of different
                 factors. In this investigation the HAZ forecasting,
                 based on the different laser cutting parameters, was
                 analyzed. The main goal was to predict the HAZ
                 according to three inputs. The purpose of this research
                 was to develop and apply the Extreme Learning Machine
                 (ELM) to predict the HAZ. The ELM results were compared
                 with genetic programming (GP) and artificial neural
                 network (ANN). The reliability of the computational
                 models were accessed based on simulation results and by
                 using several statistical indicators. Based upon
                 simulation results, it was demonstrated that ELM can be
                 used effectively in applications of HAZ forecasting.",
  keywords =     "genetic algorithms, genetic programming, Extreme
                 Learning Machine, Forecasting, HAZ, Laser cutting",
}

Genetic Programming entries for Obrad Anicic Srdan Jovic Hivzo Skrijelj Bogdan Nedic

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