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-Artificial Neural Network- Chapter 2 Basic Model

-Artificial Neural Network- Chapter 2 Basic Model. 朝陽科技大學 資訊管理系 李麗華 教授. Introduction to ANN Basic Model. Input layer Hidden layer Output layer Weights Processing Element(PE) Learning Recalling Energy function. W ij. Y 1. ‧ ‧ ‧. ‧ ‧ ‧. ‧ ‧ ‧. Y. Y j. X 1. X 2. X n.

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-Artificial Neural Network- Chapter 2 Basic Model

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  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

  3. Wij Y1 ‧ ‧ ‧ ‧ ‧ ‧ ‧ ‧ ‧ Y Yj X1 X2 Xn 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

  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

  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. 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. 1 net j > 0 if netj=0 Hopfield-Tank fc. Ynj= Yn-1j 1 1 1 0 net j<0 1 net j > 0 Signum fc. if Yj = 0 0 0 -1 net j<=0 -1 -1 -1 Transfer Functions (1/3) • Discrete type transfer function: 1 net j > 0 Step function or perceptron fc. Yj= if 0 net j <=0

  8. 1 net j > 0 Yj = if Signum0 fc. 0 netj = 0 -1 net j<0 1 1 1 net j > 0 Ynj = net j = 0 if Yn-1j -1 net j<0 0 0 -1 -1 Transfer Functions (2/3) • Discretetype transfer function: BAM fc.

  9. Transfer Functions (3/3) • Linear type: • Nonlinear type transfer function: Yj = net j net j net j > 0 Yj = if 0 net j <=0 Yj = Sigmoid function Yj = Hyperbolic Tangent function

  10. E = where E is the energy value ΔW= this is the value for adjusting weight Wij E = Energy function (a) The energy function for supervised network learning: (b) The energy function for unsupervised network learning: ΔW= this is the value for adjusting weight Wij

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