# A Recurrent Neural Network for Modelling Dynamical Systems

### Coryn A.L. Bailer-Jones, David J.C. MacKay, Philip J. Withers

We introduce a recurrent network architecture for modelling a
general class of dynamical systems. The network is
intended for modelling real-world processes in which empirical
measurements of the external and state variables are obtained at
discrete time points. The model can learn from multiple temporal
patterns, which may evolve on different timescales and be sampled at
non-uniform time intervals. We demonstrate the application of the
model to a synthetic problem in which target data are only provided
at the final time step. Despite the sparseness of the training data,
the network is able not only to make good predictions at the final
time step for temporal processes unseen in training, but also to
reproduce the sequence of the state variables at earlier times.
Moreover, we show how the network can infer the existence and role
of state variables for which no target information is provided. The
ability of the model to cope with sparse data is likely to be useful
in a number of applications, particularly the modelling of metal
forging.

Network: Computation in Neural Systems, 9(4), 533, 1998

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