Theoretically a multilayer network is capable of learning any mathematical function.
Neural networks turn out to be a variation of the normal linear classifiers.
Unlike most other methods, neural networks don't make substantial simplifying assumptions, and the method directly attempts to find weights that minimize the mean squared error distance. However, in practice, the training procedure and the design of the architecture could be very difficult and time consuming. Recently several authors have used genetic algorithms to solve these problems. A layered feed-forward neural network has layers, or subgroups of elements. A layer of processing elements makes independent computations on data that it receives and passes the results to another layer.