1. Neural Networks Dr. Peter Phillips

2. Neural Networks What are Neural Networks Where can neural networks be used Examples Recognition systems (Voice, Signature, Face, Sonar etc). Condition Monitoring. Stock Market Prediction/ Medical

3. The Human Brain

4. A Classic Artificial Neuron  X1X1 X2X2 X3X3 SjSj Output W1W1 W2W2 W3W3 f(S j )

5. A Simple Artificial Neuron  1 0 1  1 0.5 -2 

6. Hyperplane Partitions

7. Teaching a Neural Network There are several types of neural networks and different ways of training them. Basically training can take the form of Supervised or Unsupervised training.

8. Multi layered Perceptron (MLP) Input Signals (external Stimuli) Output Values Adjustable weights

9. A word of Warning

10. Summary Neural networks solve problems by mimicking the workings of the human brain. They have the ability to generalise. They have the ability to learn, you cannot programme a neural network to solve a problem. They are used extensively in the real world