Pattern Recognition and Classification Neural networks are able to recognize underlying patterns and predict outcomes based on incomplete or inconsistent information. In contrast to expert systems, which use a static set of rules, neural networks are able to learn and adapt to changes in the environment.
Neural networks resemble the human brain because they can learn.
A back-propagation neural network develops its predictive capabilities by being trained on a set of historical inputs and known resulting outputs. The neural net applies random weights to each designated input variable. It then adjusts the weights depending on how closely the actual output values match the desired output values in the training set of historical data. Once the appropriate variable weights have been set that minimize the difference between expected and actual output from the neural net, the neural net can then be applied to new data for classification. Back-propagation has become the most widely used neural network paradigm for modeling, forecasting, and classification.