Networking UK

Neural Networks

A neural network is a computer array that works in a similar way to the neural network in a brain. The main advantage of neural networks is the way they copy the brain's pattern-recognition abilities and, for example, neural networks have been utilised for forming investment decisions, recognizing handwriting, and even bomb detection.

A common factor in neural networks is that data is held within the network itself instead of being introduced via the programme. The network then capable of learning through experience. Neural networks can do this because they are made from processing elements - ie artificial neurons - which are in layered groups. The input layer is able to receive data from it's surroundings, and the output layer passes on the reaction; between these two layers may be one or several layers with no point of contact contact with the surroundings, where most of the data processing is carried out. A neural network's output is reliant upon the mass of the interfaces between neurons in the separate layers and each of the masses is proportionate to the relevance of one particular connection. When the sum total of the mass of the inputs which a certain neuron receives exceeds a particular given value that neuron will communicate a message to each interfacing neuron in the neuron to which it is connected in the adjacent layer. It can be seen for example, therefore, that a neural network could be used to determine the result of an application for a loan in which the inputs correspond to positive or negative loan application data and the output is a decision on whether or not the loan should be granted.

To go a stage further: firstly a network can be equipped with a data feedback system (ie a reverse propagation algorithm), that allows it to be trained to learn, from examples, to change the the connection masses back through the network. Secondly, oscillating neural networks can be created which respond to data coming from multiple directions including paths both within and between the layers, which gives the capability of far more complex neural relationships.

Normally, a neural network is taught by supplying it with certain fixed data and providing the expected response. Once the newtwork has 'learned' a certain response a more complex one can be fed into it. On the other hand a network can be simply be given a whole mass of data and left by itself to find repetitive patterns within this data; an obvious use for this would be large scale statistical analyses.

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