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

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

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

6. Error Backpropagation
Out 1
2. For each of the examples:
2.5. Compute and record weight change
for each connection -1
0.025
0.5
0.1
0.377 A C
-0.2
 A->C
0.0000
 A->D
 B->C
 B->D
 C->E
 D->E
-0.1 E
0.5094
0.05
0.3 D
0.377 B
0.2
0.5

7. Error Backpropagation
Out 1
3. After processing all examples update weight
4. Repeat process until obtaining “good” weights -1
0.025
0.5
0.1
0.377 A C
-0.2
 A->C
0.0001
 A->D
 B->C
0.0004
 B->D
 C->E
0.3853
 D->E
-0.049
-0.1 E
0.5094
0.05
0.3 D
0.377 B
0.2
0.5