Ajiboye, A.R.Mabayoje, M.A.Adewole, K.S.2023-05-122023-05-1220182141-0720https://uilspace.unilorin.edu.ng/handle/20.500.12484/10204Multilayer back-propagation neural networks are the network structures build with distinct layers. Due to some inherent challenges of single-layered network in solving some nonlinear problems, it is desirable to have hidden layer(s) with adequate number of neurons; as this would give more processing power to the network. The main objective of this study was to unveil the architecture that can produce most accurate predictive model among the multilayer back-propagation neural networks that is being considered. This study specifically measures and compares the accuracies of the models created using the feed-forward back-propagation, cascade-forward back-propagation and Elman back-propagation neural networks. The required data sets for implementation were retrieved from the online public repositories. Experiments were conducted and repeated using two sets of different data in order to establish the consistencies of the network outputs. Findings from this study are based on a number metrics, and the results show that, among the three architectures being considered, the predictive model created using the feed-forward neural network architecture records the lowest error and found to converge at the lowest epoch.enComputer ScienceArticle