The binary-weights neural network for robot control

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

@InProceedings{Li:2010:BioRob,
  author =       "Shuguang Li and Jianping Yuan and Xiaokui Yue and 
                 Jianjun Luo",
  title =        "The binary-weights neural network for robot control",
  booktitle =    "3rd IEEE RAS and EMBS International Conference on
                 Biomedical Robotics and Biomechatronics (BioRob 2010",
  year =         "2010",
  month =        "26-29 " # sep,
  pages =        "765--770",
  abstract =     "We propose a pure topological recurrent networks
                 controller, which has random binary connections in
                 hidden layer, and all hidden neurons are activated by
                 sinusoidal functions. A direct graph encoding method
                 and four genetic operators are implemented for using
                 genetic programming to train this controller. Firstly,
                 its feasibility and efficiency were validated by a pair
                 of function approximation experiments, the results show
                 that through evolutionary learning, this novel RNN
                 controller can handle nonlinear problems as well as
                 common RNN even without adjustable weights. Moreover, a
                 simulated mobile robot was equipped with this
                 controller, and the robot was navigated around
                 obstacles toward a goal in physical simulation
                 environments; during tests, this robot exhibited four
                 successful behaviours just by topological evolving on
                 the simple controller. This experiment reveals that
                 this controller has the simplicity, usability and
                 potential for robot control, it then raises the hope
                 for further works in exploring network motifs from high
                 level controllers.",
  keywords =     "genetic algorithms, genetic programming, binary-weight
                 neural network, direct graph encoding, evolutionary
                 learning, genetic operator, navigation, nonlinear
                 problems, obstacle avoidance, random binary connection,
                 robot control, simulated mobile robot, sinusoidal
                 functions, topological recurrent artificial neural
                 network controller, collision avoidance, encoding,
                 learning (artificial intelligence), mobile robots,
                 neurocontrollers, random functions, recurrent neural
                 nets",
  DOI =          "doi:10.1109/BIOROB.2010.5626893",
  ISSN =         "2155-1774",
  notes =        "is this GP? Northwestern Polytech. Univ., Xi'an,
                 China. Also known as \cite{5626893}",
}

Genetic Programming entries for Shu-guang Li Jianping Yuan Xiaokui Yue Jianjun Luo

Citations