1. An
Illustrative
Example

2. Apple/Banana Sorter

3. Prototype Vectors Measurement Vector Prototype Banana Prototype Apple
Shape: {1 : round ; -1 : eliptical}
Texture: {1 : smooth ; -1 : rough}
Weight: {1 : > 1 lb. ; -1 : < 1 lb.}

4. Perceptron

5. Two-Input Case
Decision Boundary

6. Apple/Banana Example The decision boundary should
separate the prototype vectors.
The weight vector should be orthogonal to the decision boundary, and should point in the direction of the vector which should produce an output of 1. The bias determines the position of the boundary

7. Testing the Network
Banana:
Apple:
“Rough” Banana:

9. Hamming Network

10. For Banana/Apple Recognition
Feedforward Layer
For Banana/Apple Recognition

11. Recurrent Layer

12. Hamming Operation
First Layer
Input (Rough Banana)

13. Hamming Operation
Second Layer

14. Hopfield Network

15. Apple/Banana Problem
Test: “Rough” Banana
(Banana)

16. Summary Perceptron Hamming Network Hopfield Network
Feedforward Network
Linear Decision Boundary
One Neuron for Each Decision
Hamming Network
Competitive Network
First Layer – Pattern Matching (Inner Product)
Second Layer – Competition (Winner-Take-All)
# Neurons = # Prototype Patterns
Hopfield Network
Dynamic Associative Memory Network
Network Output Converges to a Prototype Pattern
# Neurons = # Elements in each Prototype Pattern

17. The End of Chapter III