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ScheelPresentation - Backpropagation Through Time Alexander...

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Unformatted text preview: Backpropagation Through Time Alexander Scheel ME 697Y February 14, 2012 ME 697Y ‐ Alexander Scheel ‐ 02/14/2012 2 AGEND A 1. Recap: Recurrent Neural Networks 2. Training Methods 3. Backpropagation Through Time (BPTT) 4. Example ME 697Y ‐ Alexander Scheel ‐ 02/14/2012 3 1. RECAP: RNN Input Layer Hidden Layer Output Layer Recurrent Neural Network Feedforward Neural Network Input Layer Hidden Layer Output Layer context nodes • static • cannot capture effect of previous inputs and states • feedback conncetions • dynamic systems with states • applications: dynamic modelling, time series prediction, etc. ME 697Y ‐ Alexander Scheel ‐ 02/14/2012 4 2. TRAINING METHODS • cannot simply apply backpropagation because of feedback • a different training method is needed • several algorithms exist for training RNN: − Real Time Recurrent Learning, recursive, used in on ‐ line applications − Dynamic Backpropagation − Backpropagation Through Time y(1) y(2) y(3) y(4) y(k)...
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This note was uploaded on 02/22/2012 for the course ME 697 taught by Professor Staff during the Fall '08 term at Purdue.

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ScheelPresentation - Backpropagation Through Time Alexander...

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