Where are Neural Networks applicable?

Neural networks cannot do anything that cannot be done using traditional computing techniques, BUT they can do some things which would otherwise be very difficult.

In particular, they can form a model from their training data (or possibly input data) alone.

This is particularly useful with sensory data, or with data from a complex (e.g. chemical, manufacturing, or commercial) process. There may be an algorithm, but it is not known, or has too many variables. It is easier to let the network learn from examples.

Neural networks are being used:

in investment analysis:

to attempt to predict the movement of stocks currencies etc., from previous data. There, they are replacing earlier simpler linear models.

in signature analysis:

as a mechanism for comparing signatures made (e.g. in a bank) with those stored. This is one of the first large-scale applications of neural networks in the USA, and is also one of the first to use a neural network chip.

in process control:

there are clearly applications to be made here: most processes cannot be determined as computable algorithms. Newcastle University Chemical Engineering Department is working with industrial partners (such as Zeneca and BP) in this area.

in monitoring:

networks have been used to monitor

• the state of aircraft engines. By monitoring vibration levels and sound, early warning of engine problems can be given.
• British Rail have also been testing a similar application monitoring diesel engines.

in marketing:

networks have been used to improve marketing mailshots. One technique is to run a test mailshot, and look at the pattern of returns from this. The idea is to find a predictive mapping from the data known about the clients to how they have responded. This mapping is then used to direct further mailshots.

Where are neural networks going?

This ranges from basic research into new and more efficient learning algorithms, to networks which can respond to temporally varying patterns (both ongoing at Stirling), to techniques for implementing neural networks directly in silicon. Already one chip commercially available exists, but it does not include adaptation. Edinburgh University have implemented a neural network chip, and are working on the learning problem.

Production of a learning chip would allow the application of this technology to a whole range of problems where the price of a PC and software cannot be justified.

There is particular interest in sensory and sensing applications: nets which learn to interpret real-world sensors and learn about their environment.