Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002 Lecture 4 February 14, 2002 Shreekanth Mandayam Robi Polikar ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring02/ann/

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

Recall: Multilayer Perceptron (MLP) Architecture 1 j x1 x2 x3 y1 y2 wij wjk wkl Input Layer Hidden Layers Output Inputs Outputs

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

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

MLP Implementation http://engineering.rowan.edu/~shreek/spring02/ann/demos/mlp.m http://engineering.rowan.edu/~shreek/spring02/ann/lab2.html

Summary