Artificial Neural Network

EE105

6조

Artificial Neural Network (ANN)

• A mathematical model inspired by biological

neural networks

• ANN consists of an interconnected group of

artificial neurons

▶

Compare to Brain

Biological Neural Network Artificial Neural Network

Neuron Unit (or node)

Synapse Connection

Inhibition or Excitation of Neuron Connection Weight

Threshold of firing rate Activation Function

ANN Structure

ANN BNN

Input Layer Sensory Neurons

Hidden Layer Interneurons

Output Layer Motoneurons

Attractions of ANN Model

• Learning

– Human brain can learn by changing their

interconnections between neurons

– ANN can learn by changing their connection

weights between units

• Parallel Processing : Many processes simultaneously

• Robustness: It works even if it is damaged

Unit

Unit

How to Work?

Unit

Unit

1

Unit

n

Unit

2

…

Example – AND Operator

Unit

1

Unit

2

Unit

3

X f(X) Y

0 0 0 0 0

0 1 0.5 0.5 0

1 0 0.5 0.5 0

1 1 1 1 1

Example – OR Operator

Unit

1

Unit

2

Unit

3

X f(X) Y

0 0 0 0 0

0 1 0.5 0.5 1

1 0 0.5 0.5 1

1 1 1 1 1

Example – XOR Operator

Unit

1

Unit

2

Unit 1 Unit 2 Unit 5

X f(X) Y

0 0 0 0 0

0 1 0.5 0.5 1

1 0 0.5 0.5 1

1 1 0 0 0

Unit

3

Unit

4

Unit

5

How to Learn?

1. Set all connection weight as randomly

2. Input the data

3. If the output corrects (expected value)

▷ Then, exit iteration

▷ Else, change the connection weights to reduce

difference and repeat (go to 2)

How to change connection weight?

There are many algorithms but is hard to explain because of the

margin of the slide is too small!!

Applications

• Pattern Recognition

– Voice Recognition

– Medical Treatment (e.g. cancer detect)

• Data Processing

– Noise Filtering

• Robotics

– Data-Driven Predictive Controller

Any Questions?

Thank you