线性和非线性自适应网络的时间序列预测

[Proceedings] 1991 IEEE International Joint Conference on Neural Networks Pub Date : 1991-11-18 DOI:10.1109/IJCNN.1991.170431

J. Coughlin, R. Baran

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引用次数: 2

摘要

对具有单个隐藏层的反向传播网络进行了训练，使其能够对各种标量时间序列进行一步预测。这种网络的性能通常等于或超过同阶的线性自适应预测器。对包括宽带海洋声环境噪声在内的周期、混沌和随机时间序列进行了线性和非线性预测的比较

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Time series prediction with linear and nonlinear adaptive networks

Backpropagation networks with a single hidden layer were trained to perform one-step prediction on a variety of scalar time series. The performance of such nets typically equals or exceeds that of the linear adaptive predictor of the same order. Comparisons of the linear and nonlinear predictors were made with periodic, chaotic, and random time series, including broadband ocean acoustic ambient noise.<>

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[Proceedings] 1991 IEEE International Joint Conference on Neural Networks

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