Self-organizing neural network for multidimensional mapping and classification of multiple valued data

A new self-organizing neural network for the recognition and prediction of multiple-valued patterns is introduced. It is a supervised learning system which incorporates two MVL ART modules (ART/sub a/ and ART/sub b/) that can learn to predict a prescribed m-dimensional output vector given a prescribed n-dimensional input vector. These MVL ART modules are linked via an inter ART mapfield that controls the learning of an associative map from ART/sub a/ recognition categories to ART/sub b/ recognition categories. Facilities are incorporated to predict the correct output vector from learned one-to-many relationships. It can be seen that, learning always converges because all adaptive weights are monotonically nonincreasing. The simulation results show that this network learns for recognition and prediction within four trials while backpropagation requires 100s of trials to perform the same task.