Recognition system

Abstract

A recognition system of the self-organizing artificial neural network type is arranged to classify input data according to stored categories which have been determined by a training process. In the training process the initial category representations are selectively iteratively updated in response to a series of training patterns and in accordance with a competitive learning routine. this routine uses measures of category utilization based on the proportion of all inputs received over a representative period, particularly long term utilisation and short term utilization, to ensure that all available categories will be used and that the system is stable. The training rate which determines the amount of modification to a category representation at an up-date is local to each category and is based upon the maturity of the category and on the similarity measure between the internal representative pattern and the training input so that the training duration can be minimized. A user-operated selectively-operable suggestion learning input is provided to each category to modify the training process or to enable secondary training to proceed during classification of input data using that input data as the training patterns. The categories are represented by multiple reference patterns with respective importance values from which the degree of compatibility between an input and a category is computed taking into account the importance values.