Transcript Neural Networks and Genetic Algorithms

Brian Merrick
3-25-10
CS498 Seminar
Introduction to
 Types of Neural
 Neural Networks
 Applications of
 Conclusion
 Questions

Neural Networks
Networks
with Pattern Recognition
Neural Networks
Inspiration for development came from
attempts to model the human central
nervous system
 Artificial network that simulates systems,
such as how the brain processes
information
 Composed of a large number of highly
interconnected processing elements
(neurons) working in unison to solve
specific problems

Each neural
network consists
of many input
nodes whose input
can to go one or
more processing
nodes to produce
output
Uses learning algorithms to compute output
values based on result of the previous
populations
 Not rule-based like a traditional system,
but trained to recognize and generalize
the relationship between a set of inputs
and outputs

Prediction
 Classification
 Data Filtering
 Supervised Learning
 Unsupervised Learning


Prediction
• Use input values to predict some output
(e.g. pick the best stocks in the market,
predict weather, identify people with cancer
risks etc.)
• Example Networks:
 Directed Random Search

Classification
• Use input values to determine the
classification (e.g. is the input the letter
A, is the blob of video data a plane and
what kind of plane is it)
• Example Networks:
 Learning Vector Quantization
 Probabilistic Neural Networks

Data Filtering
• Smooth an input signal (e.g. take the noise
out of a telephone signal)
• Example Networks:
 Recirculation
Uses a known structure with random
weights. The inputs and outputs are known
 The data set is large enough to complete
learning and can be tested later for
accuracy of computed outputs
 The network adjusts weight values to some
predetermined level of accuracy and then
stops

Seeks to determine how the data is
organized
 An answer is requested from the neural
network and weights are adjusted if the
answer is not ‘correct’
 Uses back-propagation for each iteration
in the network

To begin, the network is initialized, all
the connection strengths are set randomly,
and the network sits as a blank slate
 The network is then presented with
information and the input nodes receive a
digitized version of the image

In a gender pattern
recognizer each response
will be compared to the
correct response for
that picture (i.e., 0.0
for male, 1.0 for
female) and each
connection strength is
adjusted so that next
time it's shown that
picture
Character Recognition: handwriting
recognition, number recognition
 Image (Data) Compression:
can compress
and decompress image data
 Pattern Recognition: rare coin evaluation,
bomb sensing equipment in airports

Signal Processing: removing telephone
background noise, detecting engine
misfires by sound in real-time
 Finance: market forecasting, credit
history checks, loan approval,
telemarketing
 Systems Control: factories, refineries,
NASA space shuttle, robotics

Neural networks try to simulate tasks of
the human brain
 Neural networks are very complex to
implement because of the relationships
between the data and the learning nodes
 Unsupervised neural networks are the
closest solution to modeling the human
brain




Neural Networks.
http://www.doc.ic.ac.uk/~nd/surprise_96/jour
nal/vol4/cs11/report.html#Introduction%20to%
20neural%20networks
Artificial Neural Networks Technology.
http://nature.berkeley.edu/~bingxu/UU/
geocomp/Week14/Network%20Selection%205_0.htm
The Neural Approach to Pattern Recognition.
http://www.acm.org/ubiquity/views/v5i7_jesan.html