Self-organizing pattern classification neural network system

Claim 1

A self-organizing pattern classification neural network system for classifying incoming pattern signals into their classes, comprising: (a) feature extractors for extracting different feature vectors from an incoming pattern signal; (b) self-organizing neural network classifiers, one for each of said features extractors, for receiving the feature vectors and generating response vectors, each response vector comprising a plurality of response scalars corresponding to the respective classes wherein each said self-organizing classifier comprises: i) input nodes for receiving feature scalars of each of said feature vectors; ii) a plurality of intermediate nodes for receiving said feature scalars for said input nodes and for generating a plurality of intermediate outputs; iii) a plurality of output nodes for receiving intermediate outputs of intermediate nodes of a class, for determining a smallest intermediate output, and for transferring, to said discriminator, said smallest intermediate output as a response scalar; iv) a self-organizing selector for receiving said smallest intermediate output and a node number of said intermediate node which gives said smallest intermediate output and for determining a weights update signal based on node number and said intermediate output from said intermediate node, and a correct class signal from said learning trigger; and c) a discriminator for receiving said response vectors and generating a classification response, which includes information indicative of whether classification is possible and also includes an identified class; and d) a learning trigger for transferring a correct class signal to said self-organizing classifiers based on a class of said incoming pattern signal and based on said classification response.