-Artificial Neural NetworkChapter 3 Perceptron 朝陽科技大學 資訊管理系 李麗華教授 Outline •History •Structure •Learning Process •Recall Process •Solving OR Problem •Solving AND Problem •Solving XOR Problem 朝陽科技大學 李麗華 教授.
Download ReportTranscript -Artificial Neural NetworkChapter 3 Perceptron 朝陽科技大學 資訊管理系 李麗華教授 Outline •History •Structure •Learning Process •Recall Process •Solving OR Problem •Solving AND Problem •Solving XOR Problem 朝陽科技大學 李麗華 教授.
Slide 1
-Artificial Neural NetworkChapter 3 Perceptron
朝陽科技大學
資訊管理系
李麗華教授
Slide 2
Outline
•History
•Structure
•Learning Process
•Recall Process
•Solving OR Problem
•Solving AND Problem
•Solving XOR Problem
朝陽科技大學 李麗華 教授
2
Slide 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.
朝陽科技大學 李麗華 教授
3
Slide 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.,
n
n e t j I j , and the Bias j is used.
Output layer: the computed results is
generated through transfer function.
Transfer Function: discrete type,
i.e., step function.
朝陽科技大學 李麗華 教授
4
Slide 5
Perceptron Network Structure
W11
X1
f1
W13
W12
f3
Y1
W21
X2
W22
f2
朝陽科技大學 李麗華 教授
W23
5
Slide 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: j
3. Input training sets Xi (preparing Tj for verification )
4. Training computation:
net
j
W
ij
X i
j
i
1
Y j=
net j > 0
if
0
net j 0
朝陽科技大學 李麗華 教授
6
Slide 7
The training process
5. Training computation:
If T Y 0 than:
j
j
W ij T j Y j X i
j
T j Y j
Update weights and bias :
W ij W ij W ij
new j j
j
6. repeat steps 3 ~step 5 until every input pattern is satisfied as:
T
j
Y j 0
朝陽科技大學 李麗華 教授
7
Slide 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, Wij, and the bias, θj , is used to
derive netj and, therefore, the output Yj can be
obtained for pattern recognition(or for prediction).
朝陽科技大學 李麗華 教授
8
Slide 9
Slide 10
Slide 11
Slide 12
朝陽科技大學 李麗華 教授
12
Slide 13
Slide 14
Example: Solving the AND problem
•This is a problem for recognizing the AND pattern
•Let the training patterns are used as follow
X1
X2
T
0
0
0
0
1
0
1
0
0
1
1
1
X2
朝陽科技大學 李麗華 教授
f1
X1
14
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Slide 17
朝陽科技大學 李麗華 教授
17
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朝陽科技大學 李麗華 教授
18
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朝陽科技大學 李麗華 教授
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朝陽科技大學 李麗華 教授
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朝陽科技大學 李麗華 教授
24
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朝陽科技大學 李麗華 教授
25
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朝陽科技大學 李麗華 教授
26
-Artificial Neural NetworkChapter 3 Perceptron
朝陽科技大學
資訊管理系
李麗華教授
Slide 2
Outline
•History
•Structure
•Learning Process
•Recall Process
•Solving OR Problem
•Solving AND Problem
•Solving XOR Problem
朝陽科技大學 李麗華 教授
2
Slide 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.
朝陽科技大學 李麗華 教授
3
Slide 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.,
n
n e t j I j , and the Bias j is used.
Output layer: the computed results is
generated through transfer function.
Transfer Function: discrete type,
i.e., step function.
朝陽科技大學 李麗華 教授
4
Slide 5
Perceptron Network Structure
W11
X1
f1
W13
W12
f3
Y1
W21
X2
W22
f2
朝陽科技大學 李麗華 教授
W23
5
Slide 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: j
3. Input training sets Xi (preparing Tj for verification )
4. Training computation:
net
j
W
ij
X i
j
i
1
Y j=
net j > 0
if
0
net j 0
朝陽科技大學 李麗華 教授
6
Slide 7
The training process
5. Training computation:
If T Y 0 than:
j
j
W ij T j Y j X i
j
T j Y j
Update weights and bias :
W ij W ij W ij
new j j
j
6. repeat steps 3 ~step 5 until every input pattern is satisfied as:
T
j
Y j 0
朝陽科技大學 李麗華 教授
7
Slide 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, Wij, and the bias, θj , is used to
derive netj and, therefore, the output Yj can be
obtained for pattern recognition(or for prediction).
朝陽科技大學 李麗華 教授
8
Slide 9
Slide 10
Slide 11
Slide 12
朝陽科技大學 李麗華 教授
12
Slide 13
Slide 14
Example: Solving the AND problem
•This is a problem for recognizing the AND pattern
•Let the training patterns are used as follow
X1
X2
T
0
0
0
0
1
0
1
0
0
1
1
1
X2
朝陽科技大學 李麗華 教授
f1
X1
14
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朝陽科技大學 李麗華 教授
17
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朝陽科技大學 李麗華 教授
18
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朝陽科技大學 李麗華 教授
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朝陽科技大學 李麗華 教授
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朝陽科技大學 李麗華 教授
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朝陽科技大學 李麗華 教授
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朝陽科技大學 李麗華 教授
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