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Volume 11 Issue 6
November  1996
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Chen Ke, Bao Weiquan, Chi Huisheng. Speed up Training of the Recurrent Neural Network Based on Constrained optimization Techniques[J]. Journal of Computer Science and Technology, 1996, 11(6): 581-588.
Citation: Chen Ke, Bao Weiquan, Chi Huisheng. Speed up Training of the Recurrent Neural Network Based on Constrained optimization Techniques[J]. Journal of Computer Science and Technology, 1996, 11(6): 581-588.
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Speed up Training of the Recurrent Neural Network Based on Constrained optimization Techniques

  • National Laboratory of Machine Perception and Center for Information SciencePeking University; Beijing 100871;

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  • Published Date: November 09, 1996
    Abstract
  • In this paper, the constrained optimization technique for a substantial prob-lem is explored, that is accelerating training the globally recurrent neural net-work. Unlike most of the previous methods in feedforward neuxal networks, the authors adopt the constrained optimization technique to improve the gradiellt-based algorithm of the globally recuxrent neural network for the adaptive learn-ing rate during training. Using the recurrent network with the improved algo-rithm, some experiments in two real-world…
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    • References (3)
  • [1]
    Nerrand.O et al. Training recurrent neural networks: Why and how An illustration in dynamical processing modeling. IEEE Trans.on Neural Networks, 1994, 5(2): 178-184.
    [2]
    Williams.R.J et al. Gradient-Based Learning Algorithm for Recurrent Networks. In Back-Propagation Theory: Architectures and Applications, Chauvin Y et al.(eds.), Hillsdale, NJ: Erbaum, 1991.
    [3]
    Chan.L.W et al. An Adaptive Training Algorithm for Backpropagation Networks. Computer Speech and Language 2, 1987, pp. 205-218. ………….
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