Resilient back propagation learning algorithm for recurrent fuzzy neural networks

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Resilient back propagation learning algorithm for recurrent fuzzy neural networks

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An efficient training method for recurrent fuzzy neural networks is proposed. The method modifies the RPROP algorithm, originally developed for static neural networks, in order to be applied to dynamic systems. A comparative analysis with the standard back propagation through time is given, indicating the effectiveness of the proposed algorithm.

Inspec keywords: mean square error methods; recurrent neural nets; backpropagation; recursive functions; fuzzy neural nets

Other keywords: modified RPROP algorithm; first-order learning methods; resilient backpropagation learning algorithm; mean squared error; recursive equations; fitting parameter; recurrent fuzzy neural networks; adaptation mechanism; efficient training method; ordered partial derivatives

Subjects: Learning in AI (theory); Neural nets (theory); Interpolation and function approximation (numerical analysis)

References

    1. 1)
      • Riedmiller, M., Braun, H.: `A direct adaptive method for faster backpropagation learning: the RPROP algorithm', Proc. IEEE Int. Joint Conf. on Neural Networks, 1993, San Francisco, CA, USA, p. 586–591.
    2. 2)
    3. 3)
      • C. Igel , M. Husken . Empirical evaluation of the improved RPROP learning algorithms. Neurocomputing , 50 , 105 - 123
    4. 4)
    5. 5)
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