Efficient representation of Recurrent Neural Networks for Markovian/non-Markovian Non-linear Control Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Maryam Mahsal Khan and Gul Muhammad Khan and 
                 Julian F. Miller",
  title =        "Efficient representation of Recurrent Neural Networks
                 for Markovian/non-Markovian Non-linear Control
  booktitle =    "10th International Conference on Intelligent Systems
                 Design and Applications (ISDA 2010)",
  year =         "2010",
  month =        "29 " # nov # " -" # dec # " 1",
  pages =        "615--620",
  abstract =     "A novel representation of Recurrent Artificial neural
                 network is proposed for non-linear Markovian and
                 non-Markovian control problems. The network
                 architecture is inspired by Cartesian Genetic
                 Programming. The neural network attributes namely
                 weights, topology and functions are encoded using
                 Cartesian Genetic Programming. The proposed algorithm
                 is applied on the standard benchmark control problem:
                 double pole balancing for both Markovian and
                 non-Markovian cases. Results demonstrate that the
                 network has the ability to generate neural architecture
                 and parameters that can solve these problems in
                 substantially fewer number of evaluations in comparison
                 to earlier neuroevolutionary techniques. The power of
                 Recurrent Cartesian Genetic Programming Artificial
                 Neural Network (RCGPANN) is its representation which
                 leads to a thorough evolutionary search producing
                 generalised networks.",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Markovian-nonMarkovian nonlinear
                 control problems, evolutionary search, generalised
                 networks, neural architecture, neuroevolutionary
                 techniques, recurrent artificial neural network,
                 recurrent neural networks, standard benchmark control
                 problem, Markov processes, neurocontrollers, nonlinear
                 control systems, recurrent neural nets",
  DOI =          "doi:10.1109/ISDA.2010.5687197",
  notes =        "Also known as \cite{5687197}",

Genetic Programming entries for Maryam Mahsal Khan Gul Muhammad Khan Julian F Miller