Title: Artificial Neural Networks 0909.560.01/0909.454.01 Fall 2004
1Artificial Neural Networks0909.560.01/0909.454.01
Fall 2004
Lecture 10November 15, 2004
- Shreekanth Mandayam
- ECE Department
- Rowan University
- http//engineering.rowan.edu/shreek/fall04/ann/
2Plan
- Fuzzy Inference Systems
- Functional Equivalence with RBF
- Unsupervised Learning Other Neural Net
Architectures - Self Organizing Maps (SOMs)
- Kohonen Network
- Recurrent Networks
- Hopfield Network
- Final Project Discussion
3Classical ANN Paradigm
Stage 1 Network Training
Feedforward Artificial Neural Net
Present Examples
Stage 2 Network Testing
Feedforward Artificial Neural Net
New Data
4What if?
- Desired outputs are unknown
- Input data is partially complete
- Neural net is not just feedforward
Unsupervised Learning
Self-organizing Maps Recurrent
Networks
5Self Organizing Maps
wi
xi i(xi)
- 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
Matlab Demos Competitive learning 2-D Self
organizing map
6Recurrent NetworksWhat if?
Content Addressable Memory (CAM)
7Content Addressable Memory
The input x is stored in the equilibriumneuron
states
x
The network falls into the appropriate equilibr
ium state
Perturbed/partial x
Matlab Demo Hopfield 2-Neuron
8Summary