SFAM Neural Network
(Used For Predicting Classes)

Training begins with just one hidden node whose weights are set equal to the first record and prediction is set equal to the class of the first record. Similarily, whenever a new class is encountered a new node is created. The node whose weights best match the current input supplies the prediction, provided the degree of match exceeds the vigilance threshold value. If this prediction is correct, the weights of this winning node are adjusted toward this input. If the prediction is wrong or vigilance threshold is not acheived, a new node is created with weights and prediction equal to this record.