Artificial Neural Systems

1
Artificial Neural Systems
2
Intro

• Artificial neural systems try to process
  information in the same way as the human brain
  does.
• Traditional computer systems process data using
  the "Von Neuman" model.
• Artificial neural networks (ANNs) try to imitate
  the way the human brain is organised and the way
  the brain handles information.

3
Traditional (von neuman) computer system

• A human written program tells the computer system
  how to use inputs and follow a plan to produce
  appropriate output.
• The human programmer uses complex theories to
  make a sophisticated plan that will make the
  computer system successful.
• The instructions in the program are carried out
  one after the other at a rate of hundreds of
  millions of instructions per second (MIPS) by a
  single complex and powerful processor.
• This is NOT how a human brain is physically
  structured nor is it the method used by the brain
  to process information.

4
Human brain and neurons

• A human brain has about 200,000 neurons.
• A neuron is the type of brain cell most
  associated with intelligent behaviour.
• A neuron is quite a simple "device" but each
  neuron has connections to many many other
  neurons. The pattern of interconnection is very
  complex.
• Each neuron receives signals from other neurons
  which it may ,(or may not), pass on to other
  neurons. The brain processes information by
  creating complex patterns of signals (neural
  pathways) being "fired" around large groups of
  neurons.

5
Human brain and neurons (cont)

• Any set of signals may be passed at the same time
  as other signals are being passed and so the
  brain operates with parallel processing, in fact
  very many process may take place at the same
  time. This parallel activity helps the brain to
  be fast.

6
?Artificial Neural Network (ANN)

• The ANN tries to imitate the neurons in the human
  brain.
• The network is composed of a large number of
  highly interconnected processing elements
  (neurons) working in parallel to solve a specific
  problem. It imitates the brain.

7

• There are several layers of neurons. The ANN has
  an input layer, an output layer and one or more
  hidden layers in between.
• Data is entered at the input layer and signals
  are passed through the connections between
  layers, though some signals are stronger than
  others. Each neuron uses a calculation based on
  its input signal to produce the signal it passes
  on to the next layer.
• The output layer delivers the final results.

8

• There is no program!
• The ANN learns how to be successful by training.
• In the first training session, the ANN takes a
  completely random guess at the answer (nonsense),
  getting it wildly wrong.
• Each training session involves the ANN being
  given another problem and the ANN output is
  compared against the correct answer. The
  difference between the ANN result and the correct
  answer (error) is fed back through the ANN.

9

• The ANN uses the error feedback to alter the
  pathways between the layers, some pathways are
  made stronger, others weaker. The pathways become
  tuned to the correct answer.
• After the ANN has been trained, it will be used
  to process new unseen problems of the same type.
  Usually the ANN will have a very high success
  rate at solving the problem.
• The ANN has learned its own way for solving the
  problem.

10
Applications of ANNs

• Neural networks are best at identifying patterns
  or trends in data (pattern matching), so they are
  well suited to prediction or forecasting needs
  including
• Hand-written word recognition (used for reading
  postcodes)
• Have a look some work done at University of
  Technology in Sydney Australia
• Stock market prediction will the shares rise or
  fall, when should investors buy or sell?
• Tradescision produces ANN for market analysis,
  have a look at their website

11

• Debt risk assessment should the bank customer
  get a loan or not, what are the chances of not
  getting (all) the money back?
• Recognition of speakers (voices) in
  communications
• Diagnosis of hepatitis (a liver disease)
• Three-dimensional object recognition (finger
  print recognition)
• Facial recognition (used by modern digital
  cameras, police forces)
• a pattern is some form of sequence or repetition
• eg a person's distinctive voice pattern, a
  fingerprint, the pattern of sales for ice cream
  over 12 months of the year
• ANN is good at any application where there is
  pattern identification
• here is a military use of ANN that did not go as
  planned