Applicability of advanced computational networks to the modelling of complex geometry

This thesis describes a research effort directed at producing a computational model based on artificially intelligent cellular automata. This model was developed for the purpose of learning a mapping from an input space to an output space. A specific problem that occurs in the mining industry was used to develop and test the model's ability to learn the mapping between a three-dimensional input volume and a three-dimensional output volume. In this case, the mapping was a consequence of the industrial processes used in mining as well as the properties of the material being mined.Three main computational tools were combined in this work to form the complete mine stope prediction model. The three modules are a learning module, an optimisation module, and an overall network architecture. The overall network architecture is a 3-D lattice of cellular automata (CA) and has the capability to implicitly capture the complexities in shape that render other types of models arduous or inapplicable. The learning module uses a Discrete Time Cellular Neural Network (DTCNN) to store and recall information about a given mapping. The optimisation module uses the Simulated Annealing (SA) algorithm to perform a non-linear optimisation on the set of weights used by the DTCNN.Variations of the model, and different experiments, were performed to test and explore the model in depth. Concepts such as "Small-Worlds" and "Forgetting Factor" were investigated. The applicability of a Partial Least Squares (PLS) model as an alternative to the DTCNN transition rule was also explored