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

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@InProceedings{Khan:2010:ISDA,

• 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 Problems",
• 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

Citations