Evolutionary Multivariate Dynamic Process Model Induction for a Biological Nutrient Removal Process

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@Article{Hong:2007:ASCE,
  author =       "Yoon-Seok Timothy Hong and Byeong-Cheon Paik",
  title =        "Evolutionary Multivariate Dynamic Process Model
                 Induction for a Biological Nutrient Removal Process",
  journal =      "Journal of Environmental Engineering",
  year =         "2007",
  volume =       "12",
  month =        dec,
  pages =        "1126--1135",
  email =        "hongt@lsbu.ac.uk",
  publisher =    "ASCE",
  keywords =     "genetic algorithms, genetic programming, Grammar-based
                 genetic programming, wastewater treatment process",
  ISSN =         "0733-9372",
  DOI =          "doi:10.1061/(ASCE)0733-9372(2007)133:12(1126)",
  size =         "10 pages",
  abstract =     "This paper proposes an automatic process model
                 induction system using an evolutionary computational
                 intelligence, called grammar-based genetic programming,
                 that is specially designed to automatically discover
                 multivariate dynamic process models that best fit
                 observed process data. This automatic process model
                 induction system combines an evolutionary
                 self-organising system of genetic programming paradigm
                 with various mathematical functions for a multivariate
                 nonlinear model evolution using a grammar system via
                 the mechanism of genetics and natural selection. The
                 results demonstrate how the automatic process model
                 induction system based on grammar-based genetic
                 programming can be used to develop accurate and
                 relatively cost-effective multivariate dynamic process
                 models for the full-scale biological nutrient removal
                 process. Multivariate dynamic process models are
                 derived automatically in the form of understandable
                 mathematical formulas that enable engineers to extract
                 important knowledge hidden in the data and develop
                 better operation and control strategies.",
}

Genetic Programming entries for Yoon-Seok Hong Byeong-Cheon Paik

Citations