Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This paper is concerned with the design of a Weightless Neural Network, which decomposes a given pattern into several sets of n points, termed n-tuples. Considerable research has shown that by optimising the input connection mapping of such n-tuple networks classification performance can be improved significantly. This paper investigates the hybridisation of Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) techniques in search of better connection maps to the N-tuples. Experiments were conducted to evaluate the proposed method by applying the trained classifier to recognise hand-printed digits from a widely used database compiled by U.S. National Institute of Standards and Technology (NIST).
['M. A. Hannan Bin Azhar', 'Farzin Deravi', 'Keith R. Dimond']
Hybridisation of GA and PSO to optimise N-tuples