Modeling dynamical systems by recurrent neural networks

被引：0
|
作者：
Zimmermann, HG
Neuneier, R
机构：
来源：
DATA MINING II | 2000年 / 2卷
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暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present our experiences of time series modeling by finite unfolding in time. The advantage of this approach is that the set of learnable neural network functions is restricted by a set of regularization methods which do not constrain the essential dynamics. Keywords in this section are over- and undershooting, the analysis of cause and effect, and the estimation of the embedding dimension in a partially externally driven dynamic system.
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页码：557 / 566
页数：10
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