Optimization research on Artificial Neural network Model

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

@InProceedings{Zhao:2011:ICCSNT,
  author =       "Zhao Huanping and Lv Congying and Yang Xinfeng",
  title =        "Optimization research on Artificial Neural network
                 Model",
  booktitle =    "International Conference on Computer Science and
                 Network Technology (ICCSNT 2011)",
  year =         "2011",
  month =        "24-26 " # dec,
  volume =       "3",
  pages =        "1724--1727",
  address =      "Harbin",
  abstract =     "Optimisation Research on Artificial Neural Tree
                 Network Model is divided into two parts: optimising
                 topology structure and optimising parameters. For
                 optimising topology structure, building-block-library
                 based genetic programming algorithm, anarchical
                 variable probability vector based probabilistic
                 incremental program evolution algorithm and
                 tree-encoded based particle swarm optimisation
                 algorithm are proposed. The above algorithms can
                 effectively reduce the number of invalid individuals
                 generated in evolution process, improve the convergence
                 speed and error precision of the NTNM. For optimising
                 parameters, differential evolution algorithm is
                 introduced. It has characteristics of less parameters
                 to control, easier to implement and uneasy to fall into
                 local minimum, etc. which make it very suitable for the
                 optimisation of parameters.",
  keywords =     "genetic algorithms, genetic programming, NTNM,
                 anarchical variable probability vector, artificial
                 neural network model, artificial neural tree network
                 model, building-block-library based genetic programming
                 algorithm, convergence speed, differential evolution
                 algorithm, error precision, evolution process, invalid
                 individuals, optimisation research, optimising
                 parameters, optimising topology structure, parameter
                 optimisation, probabilistic incremental program
                 evolution algorithm, tree-encoded based particle swarm
                 optimisation algorithm, convergence, neural nets,
                 particle swarm optimisation, probability, topology,
                 trees (mathematics), vectors",
  DOI =          "doi:10.1109/ICCSNT.2011.6182301",
  notes =        "Also known as \cite{6182301}",
}

Genetic Programming entries for Huanping Zhao Congying Lv Xinfeng Yang

Citations