Simulation of a multi-dimensional pattern classifier

Current techniques for multi-dimensional pattern recognition are examined with particular emphasis on the use of artificial neural networks (ANN's). A solution in the form of a Self-Organising and Self-Adaptive (SOSA) network algorithm is devised and simulated to offer a new architecture and training methodology. This network greatly reduces training times while preserving the relationships among input elements. Furthermore, the SOSA network offers the advantage of becoming simplified as training progresses. The implications of the unique properties of the SOSA network are presented. To verify the quality of the proposed SOSA network, simulation results are obtained and presented. The SOSA network is applied to a 3-dimensional surface recognition problem.