• Number of hidden layers
• Number of hidden nodes
• Feed forward and feed backward (time dependent problems)
• Links between nodes (exist or absent of links)
Output Layer
Node j Node i
Wjk
Node k
Wik
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What is ANN: Basics
– Types of ANN
• Network structure, e.g. Figure 17.9 & 17.10 (Turban, 2000, version 5, p663)
– Step 3: Select network structure, learning algorithm, and parameters
• Set the initial weights either by rules or randomly • Rate of learning (pace to adjust weights) • Select learning algorithm (More than a hundred
Axon (output wire)
W eight W 1,2
Neuron #2
Dendrite
Axon
Synapse (control of flow of electrochemical fluids
Data signals
Neuron #3
FIGURE Three Interconnected Artificial Neurons
learning algorithms available for various situations and configurations)
3. Most popular ANN Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
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1. What & Why ANN: Artificial Neural Networks (ANN)
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2. How ANN: working principle (I)
– Step 1: Collect data
– Step 2: Separate data into training and test sets for network training and validation respectively
• ANN is an information processing technology that emulates a biological neural network.
– Neuron (神经元) vs Node (Transformation) – Dendrite (树突) vs Input – Axon (轴突) vs Output – Synapse (神经键) vs Weight
– The ultimate objectives of training: obtain a set of weights that makes all the
instances in the training data predicted as correctly as possible.
– Back-propagation is one type of ANN which can be used for classification
Part III: Advance Data Mining Techniques
Chapter 8 Neural Networks
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Content
1. What & Why ANN (8.1 Feed forward Neural Network)
2. How ANN works - working principle (8.2.1 Supervised Learning)
• Starts in 1970s, become very popular in 1990s, because of the advancement of computer technology.
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Input data
Dendrite input wire
Neuron #1
l j
l i
2 j
2 i
3 j
3 i
jk
ik
0 . 2 0 0 . 1 0 0 . 3 0 – 0 . 1 0 – 0 . 1 0 0 . 2 0 0 . 1 0 0 . 5 0
Input Layer
1.0Leabharlann Node 1W1j
W1i
W2j
0.4
Node 2
W2i
W3j
0.7
Node 3
W3i
Hidden Layer
2. How ANN works - working principle (8.2.1 Supervised Learning)
3. Most popular ANN Backpropagation Network (8.5.1 The Backpropagation Algorithm: An example)
and estimation
• multi-layer: Input layer, Hidden layer(s), Output layer
• Fully connected
• Feed forward
• Error back-propagation
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Content
1. What & Why ANN (8.1 Feed forward Neural Network)
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T a b l e 8 .1 • I n i t i a lW e i g h t V a l u e s f o r t h e N e u r a lN e t w o r k S h o w n i n F i g u r e 8 .1
WWW W W W W W