循环网络预测

Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop Pub Date : 1992-08-31 DOI:10.1109/NNSP.1992.253666

N. H. Wulff, J. Hertz

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引用次数: 5

摘要

作者利用递归神经网络研究了时间序列的外推。他们使用了R. J. Williams和D. Zipser(1989)引入的实时循环学习算法，既有一阶网络的原始形式，也有二阶网络的形式。结果表明，一阶和二阶网络都能够学习模拟Mackey-Glass级数。结果的预测质量与前馈网络相当

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Prediction with recurrent networks

The authors study extrapolation of time series using recurrent neural networks. They use the real-time recurrent learning algorithm introduced by R. J. Williams and D. Zipser (1989), both in the original form for first order nets and in a form for second order nets. It is shown that both the first order and the second order nets are able to learn to simulate the Mackey-Glass series. The prediction quality of the results is comparable to that from feedforward nets.<>

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Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop

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