Evolutionary polymorphic neural network in chemical process modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Gao:2001:CCE,
  author =       "Li Gao and Norman W. Loney",
  title =        "Evolutionary polymorphic neural network in chemical
                 process modeling",
  journal =      "Computers \& Chemical Engineering",
  year =         "2001",
  volume =       "25",
  pages =        "1403--1410",
  number =       "11-12",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 polymorphic neural network (EPNN), Neural network,
                 Process modeling",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6TFT-449TFB0-2/2/b9c50f18933d4b739a9d8a2843b45548",
  ISSN =         "0098-1354",
  DOI =          "doi:10.1016/S0098-1354(01)00708-6",
  abstract =     "Evolutionary polymorphic neural network (EPNN) is a
                 novel approach to modelling dynamic process systems.
                 This approach has its basis in artificial neural
                 networks and evolutionary computing. As demonstrated in
                 the studied dynamic CSTR system, EPNN produces less
                 error than a traditional recurrent neural network with
                 a less number of neurons. Furthermore, EPNN performs
                 networked symbolic regressions for input-output data,
                 while it performs multiple step ahead prediction
                 through adaptable feedback structures formed during
                 evolution. In addition, the extracted symbolic formulae
                 from EPNN can be used for further theoretical analysis
                 and process optimisation.",
}

Genetic Programming entries for Li Gao Norman W Loney

Citations