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Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002

Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002. Lecture 10 April 4, 2002. Shreekanth Mandayam Robi Polikar ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring02/ann/. Plan. Unsupervised Learning: “Other” Neural Net Architectures

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Artificial Neural Networks 0909.560.01/0909.454.01 Spring 2002

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  1. Artificial Neural Networks0909.560.01/0909.454.01Spring 2002 Lecture 10April 4, 2002 Shreekanth Mandayam Robi Polikar ECE Department Rowan University http://engineering.rowan.edu/~shreek/spring02/ann/

  2. Plan • Unsupervised Learning: “Other” Neural Net Architectures • Self Organizing Maps (SOMs) • Kohonen Network • Recurrent Networks • Hopfield Network • Final Project Discussion

  3. Indicate Desired Outputs Determine Synaptic Weights Predicted Outputs “Classical” ANN Paradigm Stage 1: Network Training Feedforward Artificial Neural Net Present Examples Stage 2: Network Testing Feedforward Artificial Neural Net New Data

  4. “Other” network architectures What if? • Desired outputs are unknown • Input data is partially complete • Neural net is not just feedforward Unsupervised Learning Self-organizing Maps Recurrent Networks

  5. . . . . . . . . . . . . . . . . . . . . . Neuron Lattice Input Space . . xi xj Self Organizing Maps • Location of the winning neuron is based upon the class of the input signal • Similar input signals map on to winning neurons that are located close to each other • The location and synaptic weights are determined using neuron • Competition • Cooperation • Adaptation wi xi i(xi) Matlab Demos: Competitive learning 2-D Self organizing map

  6. ANN w y x You Get Address Value Recurrent Networks:What if? Content Addressable Memory (CAM)

  7. Content Addressable Memory The input x is stored in the “equilibrium”neuron states x The network “falls” into the appropriate “equilibrium” state Perturbed/partial x Matlab Demo: Hopfield 2-Neuron

  8. Summary

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