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

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Presentation on theme: "-Artificial Neural Network- Chapter 2 Basic Model 朝陽科技大學 資訊管理系 李麗華 教授."— Presentation transcript:

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 朝陽科技大學 李麗華 教授 2 Introduction to ANN Basic Model 1.Input layer 2.Hidden layer 3.Output layer 4.Weights 5.Processing Element(PE) 6.Learning 7.Recalling 8.Energy function

3 朝陽科技大學 李麗華 教授 3 ANN Components (1/4) 1.Input layer:[X 1,X 2,…..X n ] t, where t means vector transpose. 2.Hidden layer: I j => net j => Y j 3.Output layer:Y j Three ways of generating output: normalized, competitive output, competitive learning 4.Weights :W ij means the connection value between layers W ij Y ‧‧‧‧‧‧ X1X1 X2X2 XnXn YjYj Y1Y1 ‧‧‧‧‧‧ ‧‧‧‧‧‧

4 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: 1.Discrete type 2.Linear type 3.Non-linear type

5 朝陽科技大學 李麗華 教授 5 ANN Components (3/4) 6. Learning: –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.

6 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.

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

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

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

10 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 Δ W= this is the value for adjusting weight W ij (b) The energy function for unsupervised network learning: E = Δ W= this is the value for adjusting weight W ij


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