New learning method for cellular neural networks template based on combination between rough sets and genetic programming

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@Article{journals/cas/RadwanT05,
  title =        "New learning method for cellular neural networks
                 template based on combination between rough sets and
                 genetic programming",
  author =       "Elsayed Radwan and Eiichiro Tazaki",
  journal =      "Cybernetics and Systems",
  year =         "2005",
  number =       "4",
  volume =       "36",
  pages =        "415--444",
  month =        jun,
  bibdate =      "2006-01-24",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/cas/cas36.html#RadwanT05",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0196-9722",
  DOI =          "doi:10.1080/01969720490929599",
  abstract =     "A new learning algorithm for space invariant Cellular
                 Neural Network (CNN) is introduced. Learning is
                 formulated as an optimisation problem by combining
                 rough sets and genetic programming. Rough Sets approach
                 has been selected for creating priori knowledge about
                 the actual effective cells, determining their
                 significance in classifying the output, and discovering
                 the optimal CNN structure. According to the lattice of
                 CNN architecture and depending on the priori knowledge
                 gained by rough sets, genetic programming will be used
                 in deriving the cloning template. Exploration of any
                 stable domain is possible by the current approach.
                 Details of the algorithm are discussed and several
                 application results are shown.",
}

Genetic Programming entries for Elsayied Radwan Eiichiro Tazaki

Citations