The Artificial Neural Network is a term attributed to the study of manufactured or simulated brain function.

Conventional computer technology (Von-Neumann Architecture) relies on a single processor (the CPU) doing all of the manipulation of data needed for the running of the machine. Facts are physically stored in locations in memory (they have specific addresses), and the computer can engage in a single action at once (even though it can complete tasks monumentally fast).

An artificial neural network is quite different. Each of its components (called neurons) are processors in their own right. These neurons are capable of processing information, and communicating information using schemes assumed similar to the internal signal processing of their biological counterparts. An artificial neural network can access and process multiple instructions synchronously (that is the capability of large scale parallel processing). Facts are stored in artificial neural networks as patterns of interconnections (similar to the supposed action of the human brain).

An artificial neural network based on
a step transfer function

All knowledge stored in an artificial neural network is learned (gradually through 'rote' learning, forming patterns of connections allowing existing data to be processed correctly, before moving on to prediction of data values).

An artificial neural network based on
a sigmoid transfer function

'Training' is the term usually applied to the process of getting the neural network to learn rules. It does this by reacting to a set of INPUTS to produce a set of easily predicted (or previously known) OUTPUTS. Initially, the OUTPUTS vary greatly from the correct conclusions - this variance is then fed back into the network, and it is adjusted before the next training run is performed. Commercial neural networks may take weeks (of continual running) to train. Once trained, neural networks are very fast at making correct conclusions from unseen data with an exceptionally low error rate. The number of neurons limits the capacity for 'memory' and 'reasoning', as does the pattern of interconnection.

Although currently lagging in the area of symbolic reasoning, artificial neural networks have been used with great success in such areas of computer vision, voice recognition, handwriting recognition, forecasting, data analysis, credit failure prediction and fraud detection.

Recently, neural network shells have become available as ANN implementations using conventional Von-Neuman machines to 'virtually' duplicate the analogue action of an ANN on a digital device. Current applications of ANNs is in pattern recognition (facial, speech, fingerprinting, shopfloor quality, mail sorting and video imageing).

Artificial Neural Network Exercises