1. An
Illustrative
Example

2. Apple/Orange Sorter Neural Network Sensors Sorter Apple Orange
Shape: {1 : round ; -1 : elliptical}
Texture: {1 : smooth ; -1 : rough}
Weight: {1 : > 1 lb. ; -1 : < 1 lb.}
Texture 結構 平滑 粗糙
Elliptical橢圓
LB 是 "磅" (pound) 的縮寫 1 LB= KG 1 KG = 2.205LB
Apple
Orange

3. Prototype Vectors sensors： Shape: {1 : round ; -1 : elliptical}
Texture: {1 : smooth ; -1 : rough}
Weight: {1 : > 1 lb. ; -1 : < 1 lb.}
sensors：

4. Perceptron
a = -1, n < 0
hardlims：
a = 1, n ≧0

5. Perceptron (cont.) i.e. W = [-1,1]T
(p1, p2) = (-1,2), then n = 2 a = hardlims(2) = 1
(p1, p2) = (1,-3), then n = -5 a = hardlims(-5) = -1

6. Apple/Orange Example

7. Apple/Orange Example 橘子

8. Hamming Network

9. For Orange/Apple Recognition
Feedforward Layer
For Orange/Apple Recognition
S=2
purelin：a=n

10. Feedforward Layer (cont.)
Why is it called Hamming ? The Hamming distance between two vectors is equal the number of elements that are different.
e.g. the Hamming distance between [1,-1,-1] and [1,1,1] is 2 , the Hamming distance between [1,1,-1] and [1,1,1] is 1

11. Recurrent Layer
a = 0, n < 0
poslims：
a= n, n ≧0

12. Hamming Operation
First Layer：input

13. Hamming Operation
Second Layer： ε＝0.5 橘子

14. Hopfield Network

15. Apple/Orange Problem a = -1, n <-1 satlins： a = n, -1≦n ≦1

16. Apple/Orange Problem
Test： 橘子

17. 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