Transcript Neural Networks 2

Neural Networks
Dr. Peter Phillips
The Human Brain (Recap of week 1)
A Classic Artificial Neuron (Recap cont.)
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Output
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Unsupervised Learning
• Today’s lecture will consider the use of
Self Organising Map (SOM) and
Unsupervised Learning
• Recall that Supervised Learning
matches inputs to outputs.
• Unsupervised Learning Classifies the
data into classes
The Biological Basis for Unsupervised
Neural Networks
• Major sensory and motor systems
are ‘topographically mapped’ in the
brain
–Vision: retinotopic map
–Hearing: tonotopic map
–Touch: somatotopic map
Kohonen Self-Organising Maps
• The most famous unsupervised
learning network is the Kohonen
Network.
• Neural network algorithm using
unsupervised competitive
learning
• Primarily used for organization
and visualization of complex data
Teuvo Kohonen
Understanding the Data Set
• A good understanding of the data set is
essential to use a SOM – or any network for
that matter
• A ‘distance measure’ and/or suitable
rescaling must be defined to allow
meaningful comparison
• The data must be of good quality and must
be representative of the application area
SOM - Architecture
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2d array of neurons
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Weighted synapses
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Set of input signals
(connected to all neurons in lattice)
Finding a Winner (2)
• Euclidean distance between two vectors a and b, a
= (a1,a2,…,an), b = (b1,b2,…bn), is calculated as:
d a, b 
 a
 bi 
2
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i
• i.e. Pythagoras’ Theorem
• Other distance measures
could be used, e.g.
Manhattan distance
Euclidean distance
SOM Parameters
• The learning rate and neighbourhood
function define to what extent the
weights of each node are adjusted
Neighbourhood function
Degree of
neighbourhood
Degree of
neighbourhood
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Time
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Distance from winner
Time
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Distance from winner
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Data For Tutorial Work
• Data collected from a UHT plant at
Leatherhead
• Consists of 300 cases use 150 for Training
and 150 for Testing
• Data collected with plant running in normal
state, during cleaning of exchangers and
with fault
Tutorial 2 (UHT Plant Data)
UHT plant cleaned and after a period of norm al operation returning to fault condition
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Cleaning
Normal
Fault
My settings
First run 70 epochs – learning rate 0.6 to 0.1
Second run 50 epochs – learning rate constant
0.1
First run Neighbourhood kept to 1
Second run Neighbourhood start 1 end 0
Trajan Classification
SOM – New Data
• A trained SOM can be used to classify new
input data
• The input data is classified to the node with
the ‘best’ or ‘closest’ weights
• Previous knowledge of other data samples
assigned to the same class enable inferences
to be made about the ‘new’ input sample