Download presentation

Published byDarlene Kittrell Modified over 2 years ago

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

Similar presentations

Presentation is loading. Please wait....

OK

Perceptron Lecture 4.

Perceptron Lecture 4.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on classical dances of india Ppt on area of triangle in coordinate geometry World map download ppt on pollution Best ppt on forest society and colonialism Ppt on standing order act fee Word to ppt online converter free 100% Ppt on magic lights Ppt on data handling for class 2 Ppt on review of related literature about grammar Ppt on index numbers youtube