Now showing items 1-3 of 3

  • Evolutionary algorithms for real-time artificial neural network training. 

    Jagadeesan, Ananda Prasanna; Maxwell, Grant M.; Macleod, Christopher (Springer https://dx.doi.org/10.1007/11550907_12, 2005-09-01)
    JAGADEESAN, A., MAXWELL, G. and MACLEOD, C. 2005. Evolutionary algorithms for real-time artificial neural network training. Lecture notes in computer science [online], 3697, Proceedings of the 15th international conference on artifical neural networks (ICANN 2005): formal models and their applications, 11-15 September 2005, Warsaw, Poland, part 2, pages 73-78. Available from: https://dx.doi.org/10.1007/11550907_12
    This paper reports on experiments investigating the use of Evolutionary Algorithms to train Artificial Neural Networks in real time. A simulated legged mobile robot was used as a test bed in the experiments. Since the ...
  • Incremental growth in modular neural networks. 

    Macleod, Christopher; Maxwell, Grant M.; Muthuraman, Sethuraman (Elsevier http://dx.doi.org/10.1016/j.engappai.2008.11.002, 2009-06)
    MACLEOD, C., MAXWELL, G. M., and MUTHURAMAN, S., 2009. Incremental growth in modular neural networks. Engineering Applications of Artificial Intelligence, 22 (4/5), pp. 660-666.
    This paper outlines an algorithm for incrementally growing Artificial Neural Networks. The algorithm allows the network to expand by adding new sub-networks or modules to an existing structure; the modules are trained using ...
  • Real time evolutionary algorithms in robotic neural control systems. 

    Jagadeesan, Ananda Prasanna (The Robert Gordon University School of Engineering, 2006)
    This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context “on-line” means while it is in use). Traditionally, Evolutionary Algorithms ...