1. -Artificial Neural Network- Chapter 3 Perceptron 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 Outline History Structure Learning Process Recall Process Solving OR Problem Solving AND Problem Solving XOR Problem

3. 朝陽科技大學 李麗華 教授 3 History of Perceptron Model In 1957, Rosenblatt and several other researchers developed perceptron, which used the similar network as proposed by McCulloch, and the learning rule for training network to solve pattern recognition problem. (*) But, this model was later criticized by Minsky who proved that it cannot solve the XOR problem.

4. 朝陽科技大學 李麗華 教授 4 Structure The network structure includes: Input layer: input variables with binary type information. The number of node depends on the problem dimension. Processing node: uses linear activation function, i.e.,, and the Bias is used. Output layer: the computed results is generated through transfer function. Transfer Function: discrete type, i.e., step function.

5. 朝陽科技大學 李麗華 教授 5 Perceptron Network Structure W 21 W 12 W 11 X1X1 X2X2 Y1Y1 f1f1 f2f2 f3f3 W 22 W 13 W 23

6. 朝陽科技大學 李麗華 教授 6 The training process The training steps: (One layer at a time) 1. Choose the network layer, nodes, and connections. 2. Randomly assign weights: Wij & bias: 3. Input training sets X i (preparing T j for verification ) 4. Training computation: 1 net j > 0 if Yj=Yj= 0 net j 0

7. 朝陽科技大學 李麗華 教授 7 The training process 5. Training computation: If than: 6. repeat steps 3 ~step 5 until every input pattern is satisfied as: Update weights and bias :

8. 朝陽科技大學 李麗華 教授 8 The recall process After the network has trained as mentioned above, any input vector X can be send into the Perceptron network to derive the computed output. The ratio of total number of corrected output is treated as the prediction performance of the network. The trained weights, W ij, and the bias, θ j, is used to derive net j and, therefore, the output Y j can be obtained for pattern recognition(or for prediction).

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14. 朝陽科技大學 李麗華 教授 14 Example: Solving the AND problem This is a problem for recognizing the AND pattern Let the training patterns are used as follow X1X1 X2X2 T 000 010 100 111 f1f1 X2X2 X1X1

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