FEED-FORWARD AND RECURRENT NEURAL NETWORKS FOR SYSTEM IDENTIFICATION
T. L. Burrows and M. Niranjan
Neural networks with nonlinear sigmoid functions at the hidden nodes have been shown to give improved performance over linear models for time series prediction problems. We demonstrate that whenever the network produces a useful solution to this problem, the hidden nodes operate predominantly in the linear region of their sigmoid function, and that small excursions into the nonlinear region allow improved prediction. Using several nonlinear time series, we demonstrate that this allows us to exploit standard linear system identification techniques. For a speech prediction problem, we compare the performance of a feed-forward and recurrent architecture and in view of our observations, attribute the improved performance of the recurrent network to a parameter estimation based on output error minimisation, rather than the equation error minimisation performed by feed-forward networks and linear prediction analysis.
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