In multilayer networks, one can identify the artificial neurons of layers such that:

• Each neuron is connected with all those of the next layer
• There are no connections between neurons belonging to the same layer
• There are no connections between neurons belonging to non-adjacent layers
• The number of layers and of neurons per layer depends on the problem to be solved

The input and output layers define inputs and outputs; there are hidden layers, whose complexity realizes different behaviors of the network. Finally, the connections between neurons are represented by as many matrices are the pairs of adjacent layers. Each array contains the weights of the connections between the pairs of nodes of two adjacent layers. The feed-forward networks are networks with no loops within the layers.

Following is the graphical representation of multilayer perceptron architecture: