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
What are neural networks?
Neural networks are a branch of AI that attempts to solve problems by mimicking the workings of a human brain.
Where can neural networks be used.
The properties of neural networks define where they are useful.
In answering this question it’s useful to look again at how conventional Von Neumann computer works. They use an algorithmic approach, that is the computer follows a set of instructions in order to solve the problem. So unless the steps that a computer must follow are known the computer cannot solve the problem. Put another way, we have to understand how to solve the problem in order to write the programme. This is not the case with neural networks. They can learn complex mappings from inputs to outputs based solely on samples They learn by example and cannot be programmed to solve a problem, so where can we use neural networks.
Examples

3. The Human Brain
Since the artificial neural network mimics the human brain we now need to look at how the brain works. First it has to be said that there’s a lot about the workings of the brain that we don’t understand. What we do know is that the brain consists of around 10 billion simple processing units called neurons. On average each neuron is connected to around 1000 other neurons. These neurons collect signals from other neurons and process the data in parallel and this is the key to the human brain. The brain can process data at around 100 times per second, the Von Neumann computer processes around 1.7 Giga instructions per second, yet the brain processes data much faster. HOW? Well the brain processes data in parallel not sequentially.

4. A Classic Artificial NeuronX1 Sj
Output W1 W2 W3
f(Sj) X2
Generally speaking there are many different types of neural networks, but they all have nearly the same components.
Describe the classic artificial neuron and the various output functions with a sigmoid. X3

5. A Simple Artificial Neuron1 S
0.5 -2
The first Perceptron used a hard limiter. This simple Perceptron can only solve problems that can be linearly separated.

6. Hyperplane Partitions
A single Perceptron (i.e. output unit) with connections from each input can perform and learn a linear separation.

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
The training process works as follows:
The network is given a set of inputs for which the correct outputs are known
The output of the network is compared with the known correct outputs and the error is measured
The weights of the network are adjusted in order to reduce the output error and the training process is repeated
The training continues until the network reaches an acceptable error level on the test inputs
Beware of overtraining

9. A word of Warning
Describe classic USA tank data error

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