An important aspect of an ANN model is whether it needs

guidance in learning or not. Based on the way they learn, all

artificial neural networks can be divided into two learning

categories - supervised and unsupervised.

• In supervised learning, a desired output result for each input

vector is required when the network is trained. An ANN of the

supervised learning type, such as the multi-layer perceptron, uses

the target result to guide the formation of the neural parameters. It

is thus possible to make the neural network learn the behavior of

the process under study.

• In unsupervised learning, the training of the network is entirely

data-driven and no target results for the input data vectors are

provided. An ANN of the unsupervised learning type, such as the

self-organizing map, can be used for clustering the input data and

find features inherent to the problem.-An important aspect of an ANN model is whether it needs

guidance in learning or not. Based on the way they learn, all

artificial neural networks can be divided into two learning

categories - supervised and unsupervised.

•　In supervised learning, a desired output result for each input

vector is required when the network is trained. An ANN of the

supervised learning type, such as the multi-layer perceptron, uses

the target result to guide the formation of the neural parameters. It

is thus possible to make the neural network learn the behavior of

the process under study.

•　In unsupervised learning, the training of the network is entirely

data-driven and no target results for the input data vectors are

provided. An ANN of the unsupervised learning type, such as the

self-organizing map, can be used for clustering the input data and

find features inherent to the problem.