Neural Networks and Fuzzy Systems

1. M. Raihan
Email: rianku11@gmail.com

2. Backpropagation
Algorithm

3. Error Function
31-Oct-17
 In Perceptron, E = t – y
Since there is only one set of weights in the network, it is possible.
3

4. An Error Function
31-Oct-17 4

5. Back Propagation Algorithm
31-Oct-17 5

6. Example
31-Oct-17 6

7. Example
31-Oct-17 7

8. Example
31-Oct-17
 The goal of back propagation is to optimize the
weights so that the neural network can learn how to
correctly map arbitrary inputs to outputs.
For the rest of this example we’re going to work
with a single training set:
inputs 0.05 and 0.10
output 0.01 and 0.99
8

9. The Forward Pass
31-Oct-17 9

10. The Logistic Function
31-Oct-17 10

11. Thank You
31-Oct-17 11