Method and device using a neural network for classifying data

A device for classifying data presented in the form of data vectors, which device provides a value representing an estimated probability that a data vector belongs to a class from one or several classes which have been learned based on specimens and which comprises a neural network with several layers for processing a data vector, characterized in that the device comprises a number of neurons and synaptic weights which have been determined by the following method:

• a) selecting at least one feature from each of a plurality of known signals having at least one feature;
• b) determining a difference between the selected at least one feature for every different pair of known signals wherein the differences are represented by a plurality of difference-vectors;
• c) selecting a first one of the difference-vectors as a current difference-vector and determining the k nearest difference-vectors from said current difference vector wherein a decision domain is Created including a group of k+1 difference vectors;
• d) calculating a distribution probability for the group of difference-vectors in the decision domain;
• e) introducing a neuron corresponding to the decision domain an internal layer of neurons of the neural network;
• f) calculating a weight represented by a.sub.ij and a weight represented by b.sub.i for each difference-vector connection between an input layer of neurons and the internal layer based upon the distribution probability of the decision domain;
• g) selecting a next one of the difference-vectors as a new current difference-vector and repeating steps (c) through (g) until the last difference-vector is processed;
• and
• h) inputting a plurality of unknown signal difference-vectors into an input layer of the network and calculating a probability based upon the weighting coefficients a.sub.ij and b.sub.i that the unknown signal difference-vectors lie within one of the decision domains,
• whereby the neural network is trained to classify the differences between the unknown signal and the known signals, thus indicating the degree of correspondence between the unknown signal and the known signals.