S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2010 Shreekanth Mandayam ECE Department Rowan University.

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S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2010 Shreekanth Mandayam ECE Department Rowan University Lecture 10 November 15, 2010

S. Mandayam/ ANN/ECE Dept./Rowan UniversityPlan Unsupervised Learning: “Other” Neural Net Architectures Self Organizing Maps (SOMs) Kohonen Network Recurrent Networks Hopfield Network Final Project Discussion

S. Mandayam/ ANN/ECE Dept./Rowan University “Classical” ANN Paradigm Stage 1: Network Training FeedforwardArtificial Neural Net Present Examples Indicate Desired Outputs DetermineSynapticWeights FeedforwardArtificial Neural Net New Data Predicted Outputs Stage 2: Network Testing

S. Mandayam/ ANN/ECE Dept./Rowan University What if? Desired outputs are unknown Input data is partially complete Neural net is not just feedforward “Other”networkarchitectures Unsupervised Learning Self-organizing Maps Recurrent Networks

S. Mandayam/ ANN/ECE Dept./Rowan University 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 xjxj Input Space xixi.. Neuron Lattice x i i(x i ) wiwi Matlab Demos: Competitive learning 2-D Self organizing map

S. Mandayam/ ANN/ECE Dept./Rowan University Recurrent Networks: What if? ANN w x y YouGet Address Value Content Addressable Memory (CAM)

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

S. Mandayam/ ANN/ECE Dept./Rowan University Final Project Discussion

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