Implementing automatic learning according to the k nearest neighbor mode in artificial neural networks

Abstract

A method of achieving automatic learning of an input vector presented to an artificial neural network (ANN) formed by a plurality of neurons, using the K nearest neighbor (KNN) mode. Upon providing an input vector to be learned to the ANN, a Write component operation is performed to store the input vector components in the first available free neuron of the ANN. Then, a Write category operation is performed by assigning a category defined by the user to the input vector. Next, a test is performed to determine whether this category matches the categories of the nearest prototypes, i.e. which are located at the minimum distance. If it matches, this first free neuron is not engaged. Otherwise, it is engaged by assigning the matching category to it. As a result, the input vector becomes the new prototype with the matching category associated thereto. Further described is a circuit which automatically retains the first free neuron of the ANN for learning.