Artificial+Neural+Systems

Artificial eural systems are based on the human brain. Neurons in the brain are cells which provide us with the ability to remember, think and apply previous experience. Each neuron is connected to up to 200,000 other neurons.

Neural systems learn from experience, not from programming. They are good at pattern recognition and trend prediction.


 * ~ Conventional Computation ||~ Neural Network ||
 * < single processor ||< many processors ||
 * < fault intolerant ||< fault tolerant ||
 * < serial ||< parallel ||
 * < general to any task ||< designed per task ||

This video shows how a neural network has been designed to learn from it's previous experience

media type="youtube" key="lmPJeKRs8gE" height="385" width="480"

Some example uses of artificial neural systems:


 * speech recognition
 * diagnosis
 * 3D object recognition
 * hand writing recognition
 * facial recogniton
 * fingerprint recognition
 * credit scoring
 * debt risk assessment
 * stock market prediction

Complete the questions in this exercise:

1. How do artificial neural systems learn?

2. Investigate 3 uses of an ANS and write a paragraph describing each.

3.