Tab Article
Artificial neural networks are graphical structures whose initial purpose was to emulate the human brain. They were introduced in 1943 by McCulloch and Pits and, since then, they have evaluated in a very powerful computational tool. In 1958 Rosenblatt introduced the perceptron ? a two-layered learning network based on simple math rules. The structure of an artificial neural network consists of an input layer, an output layer and of one or more hidden layers. The nodes in the network represent the neurons, while the links represent synapses. All links are weighted and these weights are adaptable, as they can be adjusted according to a learning algorithm. The usage of artificial neural networks today is inevitable in areas such as machine learning, data mining, pattern recognition, and automation control. Typical applications include: image and voice recognition, fraud detection, biometric user authentication (e.g. fingerprint and eye retina recognition), and complex control algorithms (e.g. aircraft motion control).
