Evolutionary learning to optimise mapping in n-Tuple networks

Abstract

The use of n-Tuple networks as pattern recognition devices is well known. Networks are able to recognise and discriminate between different classes of data if each class is taught into a separate discriminator. If the different classes are too similar, however, the system can have difficulty discriminating between the classes. This paper describes a novel method of optimising the learning strategy to minimise this problem. >