Abstract
Artificial Neural Networks are massively parallel systems that are constructed from many simple processing elements called neurons. The neurons are connected via weights. This structure is inspired by the current understanding of how biological networks function. Since the 1980s, research into this field has exploded into the hive of activity that currently surrounds neural networks and intelligent systems. The work in this thesis is concerned with one particular artificial neural network: Adaptive Resonance Theory (ART). It is an unsupervised neural network that attempts to solve the stability-plasticity dilemma. The model is, however, limited by a few serious problems that restrict its use in real life situations. The network's ability to cluster consistently with uncorrupt inputs when the input is subject to even modest amounts of noise is severely handicapped. The work detailed herein attempts to improve on ART's behaviour towards noisy inputs. Novel equations are developed and described that improve on the network's performance when the system is subject to noisy inputs. One of the novel equations affecting vigilance makes a significant improvement over the originators' equations and can cope with 16% target noise before results fall to the same values as the standard equation. The novel work is tested using a real-life (not simulated) data set from the MSTAR database. Synthetic Aperture Radar targets are clustered and then subject to noise before being represented to the network. These data simulate a typical environment where a clustering or classifying module would be needed for object recognition. Such a module could then be used in an Automatic Target Recognition (ATR) system. Once the problem is mitigated, Adaptive Resonance Theory neural networks could play important roles in ATR systems due to its lack of computational complexity and low memory requirements when compared with other clustering techniques. Keywords: Adaptive Resonance Theory, clustering consistency, neural network, automatic target recognition, noisy inputs.