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Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure

Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure. Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute. A 2-layer feedforward ANN. Input hidden layer output layer. -1. -1. -1. -1. -1. -1.

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Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure

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  1. Artificial Neural Networks (ANNs) and the Error Backpropagation Procedure Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute

  2. A 2-layer feedforward ANN Input hidden layer output layer -1 -1 -1

  3. -1 -1 -1 Error Backpropagation 1. Initialize the weights to small random values 0.5 0.1 A C -0.2 -0.1 E 0.05 0.3 D B 0.2 0.5

  4. -1 -1 -1 Error Backpropagation 2. For each of the examples: 2.1. Present example to input layer 2.2. Propagate the example forward 0.5 0 0.1 0.377 A C -0.2 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 0.5

  5. -1 -1 -1 Error Backpropagation 2. For each of the examples: 2.3. Compute node errors for output layer 2.4. Compute node errors for hidden layer 0.025 0.5 0 0.1 0.377 A C -0.2 -0.5094 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 -0.0382 0.5

  6. -1 -1 -1 Error Backpropagation 2. For each of the examples: 2.5. Compute and record weight change for each connection 0.025 0.5 0 0.1 0.377 A C -0.2 -0.5094 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 -0.0382 0.5

  7. -1 -1 -1 Error Backpropagation 3. After processing all examples update weight 4. Repeat process until obtaining “good” weights 0.025 0.5 0 0.1 0.377 A C -0.2 -0.5094 -0.1 E 0.5094 0.05 0.3 D 0.377 B 0.2 0 -0.0382 0.5

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