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Neural Networks

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Neural Network Architecture


To Train a Network

This is one simple method.

  • Make some random changes to the strengths of the links between the processors.
  • Test the new network.
  • If it performs better, keep the changes.
  • If it is the same or worse, discard the changes and try again.
  • Repeat these steps until the network performance is good enough.

A method is needed to work out a performance score for measuring the quality.

OCR ANN Simulator

Comparing Conventional and Neural Network Computation


Conventional Computer

Neural Network


  • A few inputs like a keyboard, mouse or bar code scanner.
  • Many inputs, possibly thousands.
  • For example an image recognition system would have inputs for each pixel.


  • A single processor which deals with all the data.
  • Some computers have two or four processors but rarely more.
  • Many processors, possibly thousands.
  • Each processor is much simpler than a conventional computer processor.
  • Each processor deals with a small part of the data.
  • The processors work in parallel.


  • Must be programmed by a programmer.
  • Must be trained.
  • Neural networks learn instead of being programmed.
  • Learning takes place by links being made stronger or weaker.


  • Data stored centrally (ROM, RAM, hard disk)
  • Data stored as weightings in the network.


Solves well defined problems like

  • payroll or
  • bank account management.
  • Can do precise maths.
  • Good for solving problems with small numbers of inputs and where the algorithm (set of rules) for solving the problem is well understood.

Better at predictive problems such as ...

  • Weather forecasting
  • Image recognition. Solves problems like image recognition (spot the criminal or terrorist in the shopping mall).
  • Good for solving problems with vast amounts of input data where the rules for reaching the answer are fuzzy or not well defined.




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