The aim is to devise robust computing systems which can recognize patterns and predict desired behaviour based on exposure to large amounts of data. A key feature of neural networks is their ability to learn and adapt, in a manner similar to humans. Neural networks are implemented in computer hardware and software, but unlike conventional computers which need to be explicitly programmed to perform every action, neural networks are adaptive. Once they have been supplied with a sufficient amount of data, the neural networks can adapt their response to the specific data that has been presented and provide answers based on what has been learned. A neural network constructs a model of a particular situation.
Models are the basis of many human-like tasks.
For example, in the task of navigating, we create a mental model of the route to take. Models are used frequently, and with the power and flexibility of neural network technology, it is possible to develop innovative modelling and predictive systems to find better solutions to a wide range of problems. Mathematical methods of model building have been available for many years. Linear regression is a very widely known mathematical tool for modelling data. The reason that this method derives its name however, is due to the fact that the mathematical relationship between the input data and the output produced by the model, is always linear.