FF NEURAL NETWORK

Abstract

A feedforward neural network is an artificial neural network where connections
between the units do not form a directed cycle. This is different from recurrent neural
networks. The feedforward neural network was the first and simplest type of artificial neural
network devised. In this network, the information moves in only one direction, forward, from
the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles
or loops in the network.
A feedforward neural network is a biologically inspired classification algorithm. It consist of a
(possibly large) number of simple neuron-like processing units, organized in layers. Every unit
in a layer is connected with all the units in the previous layer. These connections are not all
equal, each connection may have a different strength or weight. The weights on these
connections encode the knowledge of a network. Often the units in a neural network are also
called nodes.
Data enters at the inputs and passes through the network, layer by layer, until it arrives at the
outputs. During normal operation, that is when it acts as a classifier, there is no feedback
between layers. This is why they are called feedforward neural networks.
In the following figure we see an example of a 2-layered network with, from top to bottom: an
output layer with 5 units, a hidden layer with 4 units, respectively. The network has 3 input
units.