Modular neural network programming with genetic optimization

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@Article{Tsai201111032,
  author =       "Hsing-Chih Tsai and Yong-Huang Lin",
  title =        "Modular neural network programming with genetic
                 optimization",
  journal =      "Expert Systems with Applications",
  volume =       "38",
  number =       "9",
  pages =        "11032--11039",
  year =         "2011",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2011.02.147",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-52BGCPB-2/2/707c22583fca77726a94edea04a48c8d",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, High order neural network, ANN,
                 Concrete",
  abstract =     "This study proposes a modular neural network (MNN)
                 that is designed to accomplish both artificial
                 intelligent prediction and programming. Each modular
                 element adopts a high-order neural network to create a
                 formula that considers both weights and exponents. MNN
                 represents practical problems in mathematical terms
                 using modular functions, weight coefficients and
                 exponents. This paper employed genetic algorithms to
                 optimise MNN parameters and designed a target function
                 to avoid over-fitting. Input parameters were identified
                 and modular function influences were addressed in
                 manner that significantly improved previous practices.
                 In order to compare the effectiveness of results, a
                 reference study on high-strength concrete was adopted,
                 which had been previously studied using a genetic
                 programming (GP) approach. In comparison with GP, MNN
                 calculations were more accurate, used more concise
                 programmed formulae, and allowed the potential to
                 conduct parameter studies. The proposed MNN is a valid
                 alternative approach to prediction and programming
                 using artificial neural networks.",
}

Genetic Programming entries for Hsing-Chih Tsai Yong-Huang Lin

Citations