Neural Networks
It is difficult to find out which neurons should be connected to which. This is the problem of determining the neural network structure. Further, the interconnections in the brain are constantly changing. The initial interconnections seem to be largely governed by genetic factors. The weights on the edges and thresholds in the nodes are constantly changing. This problem has been the subject of much research and has been solved to a large extent. The approach has been as follows .
Given some input, if the neural network makes an error, then it can be determined exactly which neurons were active before the error. Then we can change the weights and thresholds appropriately to reduce this error. For this approach to work, the neural network must "know" that it has made a mistake. In real life, the mistake usually becomes obvious only after a long time. This situation is more difficult to handle since we may not know which input led to the error.
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse.