1. Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002
Lecture 4 February 14, 2002
Shreekanth Mandayam
Robi Polikar
ECE Department
Rowan University

2. Plan Multilayer Perceptron Lab Project 2
Recall: Learning rule - Backprop
Modifications of Backprop
Backprop Training Modes
Backprop Implementation - Algorithm Improvement
Lab Project 2

3. Recall: Multilayer Perceptron (MLP) Architecture1 j x1 x2 x3 y1 y2
wij
wjk
wkl
Input
Layer
Hidden Layers
Output
Inputs
Outputs

4. Recall: MLP Signal Flow
Function signal
Error signal j j j
Computations at each node, j
Neuron output, yj
Gradient vector, dE/dwji
Forward propagation
Backward propagation

5. Recall: MLP Training y x i j k Forward Pass Fix wji(n) Compute yj(n)
Left i j k
Right
Forward Pass
Fix wji(n)
Compute yj(n)
Backward Pass
Calculate dj(n)
Update weights wji(n+1) x y i j k
Left
Right

6. MLP Implementation

7. Summary