USING ARTIFICIAL NETWORKS IN COMPLEX PROBLEMS ANALYSING PARAMETERS INFLUENCE

Abstract: Mathematical statistical models are insufficient for describing complex phenomena. In contrast, Artificial Neural Networks (ANNs), have been used across various complex problem domains for solving problems. ANNs can learn complex patterns and capture non-linear relationships between parameters. Using ANNs to gain an understanding of complex problem domains can reveal hidden truths and lead to scientific discoveries not possible before with mathematical statistical models. In this thesis, a fully connected feed-forward neural network was built to analyse the parameter influence in the complex problem domain of football. The aim of this work was to demonstrate that a simple artificial neural network could be used to analyse parameter influence in complex problem domains. The investigation centred around the question of: How well can the fully connected feed-forward neural network be used for analysing parameter influence. To conduct this research, free publicly available statistical match data was gathered from online sources. Subsequently, an ANN model was built and trained to predict the outcomes of the Spanish La Liga matches during the 2021/2022 season. The network could achieve an average accuracy of 51.57\%, comparable to similar models in related studies. After the network was trained the weights were analysed to understand the influence of parameters on the outcomes of matches. The results obtained were random, indicating that this specific approach taken, requires a larger dataset. A different approach with a different type of network would be more suitable for this undertaking.