A dynamic learning rate for back-propagation training of artificial neural networks is proposed as a weighted average of direction cosines of the incremental weight vectors of the current and previous steps. Experiments on training an EEG-based sleep state pattern recognition scheme have demonstrated its improved performance.>
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Hsin et al. (1995) studied this question.
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