In unsupervised learning, the network teaches itself by emulating elements of the examples that are provided as input. As the network receives more input, it adjusts its weights trying to complete and reproduce the patterns it perceives within the data. Kohonen developed this learning style, and networks using it are considered to be self-organizing. When it discovers input that is different from what it has previously experienced, it is considered to be additional knowledge, and the synaptic weights are changed to reflect its incorporation into the system. This differs from the off-line category in which the weights are fixed during training, and do not change again.
Initial training is done with a series of examples called a training data set.
This data is repeatedly presented to the network, and an equation is used to modify the synaptic weights. Although there are many such equations, most can be considered to be variations of one of four basic rules. It was inspired by the biological observations of Canadian psychologist Donald Hebb, in 1949. He stated that when the axon of cell A is near enough to excite a cell B and repeatedly or persistently takes place in firing it, some growth process or metabolic change takes place in one or both cells such that A s efficiency, as one of the cells firing B, is increased. This equation demonstrates that the output from j sends the output to i .