Function prediction using recurrent neural networks

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Abstract
The real-time recurrent learning (RTRL) algorithm was modified and applied to the task of function prediction. This recurrent neural network was modified to include both a variable learning rate, and a linear output combined with sigmoidal hidden units. The simple learning rate modification allows faster network convergence while avoiding most cases of catastrophic divergence. In addition, a linear output combined with hidden sigmoidal units enables the network to predict unbounded functions. The modified recurrent network was then used to simulate a linear system (second order Butterworth filter). In addition, the recurrent network was applied to two specific applications: predicting 3-D head position in time, and voice data reconstruction. The accuracy at which the network predicted the pilot's head position was compared to the best linear statistical prediction algorithm. The application of the network to the reconstruction of voice data showed the recurrent network's ability to learn temporally encoded sequences, and make decisions as to whether or not a speech signal sample was considered a fricative or a voiced portion of speech.

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