S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2006 Shreekanth Mandayam ECE Department Rowan University.

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S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2006 Shreekanth Mandayam ECE Department Rowan University Lecture 4 October 9, 2006

S. Mandayam/ ANN/ECE Dept./Rowan UniversityPlan Recall: Multilayer Perceptron Architecture Signal Flow Learning rule - Backpropagation Lab Project 2

S. Mandayam/ ANN/ECE Dept./Rowan University Multilayer Perceptron (MLP): Architecture         x1x1 x2x2 x3x3 y1y1 y2y2   w ji w kj w lk Input Layer Hidden Layers Output Layer Inputs Outputs

S. Mandayam/ ANN/ECE Dept./Rowan University MLP: Characteristics Neurons possess sigmoidal (logistic) activation functions Contains one or more “hidden layers” Trained using the “backpropagation” algorithm MLP with 1-hidden layer is a “universal approximator”  (t) t

S. Mandayam/ ANN/ECE Dept./Rowan University MLP: Signal Flow Function signal Error signal    Computations at each node, j Neuron output, y j Gradient vector, dE/dw ji Forward propagation Backward propagation

S. Mandayam/ ANN/ECE Dept./Rowan University MLP Training Forward Pass Fix w ji (n) Compute y j (n) Backward Pass Calculate  j (n) Update weights w ji (n+1) i j k Left Right i j k Left Right x y

S. Mandayam/ ANN/ECE Dept./Rowan University Lab Project 2 /fall06/ann/lab2.htmlhttp://engineering.rowan.edu/~shreek /fall06/ann/lab2.html

S. Mandayam/ ANN/ECE Dept./Rowan UniversitySummary