Modeling Dynamical Systems By Recurrent
Neural Networks
Author(s)
H.G. Zimmermann & R. Neuneier
Abstract
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.
1 Introduction
Standard analysis of dynamic systems by feedforward neural networks translates
the time series identification problem into a pattern recognition approach. Typi-
cally, the first step in such an analysis looks for an appropriate description of the
present time state which can be used as an input vector. This is usually very tricky
because one has to consider many pre
Keywords
Related Book
Other papers in this volume
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