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Multilayer Perceptron (MLP) Architecture and Learning Rule Overview

Explore Multilayer Perceptron (MLP) architecture with hidden layers, signal flow, and backpropagation learning rule. Learn about neurons' activation functions, training process, and how MLP serves as a universal approximator. Follow the signal flow and computations in MLP training and lab project examples.

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Multilayer Perceptron (MLP) Architecture and Learning Rule Overview

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  1. Artificial Neural NetworksECE.09.454/ECE.09.560Fall 2006 Lecture 4October 9, 2006 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall06/ann/

  2. Plan • Recall: Multilayer Perceptron • Architecture • Signal Flow • Learning rule - Backpropagation • Lab Project 2

  3. Hidden Layers Input Layer j j Output Layer 1 x1 j j j y1 1 Outputs x2 Inputs j j j y2 1 x3 wlk j j wji wkj Multilayer Perceptron (MLP): Architecture

  4. 1 j(t) 0.5 0 -1 1 t 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”

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

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

  7. Lab Project 2 • http://engineering.rowan.edu/~shreek/fall06/ann/lab2.html

  8. Summary

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