1. -Artificial Neural Network- Chapter 2 Basic Model
朝陽科技大學
資訊管理系
李麗華 教授

2. Introduction to ANN Basic Model
Input layer
Hidden layer
Output layer
Weights
Processing Element(PE)
Learning
Recalling
Energy function
朝陽科技大學 李麗華 教授

3. ANN Components (1/4)
Input layer:[X1,X2,…..Xn]t , where t means vector transpose.
Hidden layer: I j => net j => Y j
Output layer:Yj
Three ways of generating output: normalized, competitive output, competitive learning
Weights :Wij means the connection value between layers
Wij Y ‧ X1 X2 Xn Yj Y1
朝陽科技大學 李麗華 教授

4. ANN Components (2/4) 5. Processing Element(PE)
(A)Summation Function: (supervised)
or (unsupervised)
(B)Activity Function: or or
(C)Transfer Function:
Discrete type
Linear type
Non-linear type
朝陽科技大學 李麗華 教授

5. ANN Components (3/4) 6. Learning: 7. Recalling:
Based on the ANN model used, learning is to adjust weights to accommodate a set of training pattern in the network.
7. Recalling:
Based on the ANN model used, recalling is to apply the real data pattern to the trained network so that the outputs are generated and examined.
朝陽科技大學 李麗華 教授

6. ANN Components (4/4) 8. Energy function:
Energy function is a verification function which determines if the network energy has converged to its minimum. Whenever the energy function approaches to zero, the network approaches to its optimum solution.
朝陽科技大學 李麗華 教授

7. Transfer Functions (1/3)
Discrete type transfer function: 1 -1
net j > 0
Step function or
perceptron fc.
Yj= if
net j <=0 1 -1
Hopfield-Tank fc.
net j > 0
net j<0 if
Ynj=
Yn-1j
netj=0 1 -1
net j<=0
net j > 0 if
Yj =
Signum fc.
朝陽科技大學 李麗華 教授

8. Transfer Functions (2/3)
Discrete type transfer function: 1 -1
net j > 0
net j<0 if
Yj =
netj = 0
Signum0 fc. 1 -1
net j > 0
net j<0 if
Ynj =
net j = 0
Yn-1j
BAM fc.
朝陽科技大學 李麗華 教授

9. Transfer Functions (3/3)
Linear type:
Nonlinear type transfer function:
Yj = net j
net j net j > 0
Yj = if
net j <=0
Yj =
Sigmoid function
Yj =
Hyperbolic Tangent function
朝陽科技大學 李麗華 教授

10. Energy function
(a) The energy function for supervised network learning:
E = where E is the energy value
ΔW= this is the value for adjusting weight Wij
(b) The energy function for unsupervised network learning:
E =
ΔW= this is the value for adjusting weight Wij
朝陽科技大學 李麗華 教授