Backpropagation neural network training algorithm analysis / Shahrul Azmi Rosli

• Main Author: Rosli, Shahrul Azmi
• Format: Thesis
• Language: English
• Published: 2010
• Online Access: https://ir.uitm.edu.my/id/eprint/79846/1/79846.pdf; https://ir.uitm.edu.my/id/eprint/79846/

Neural network is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. Backpropagation, or propagation of error, is a common method of teaching artificial neural networks how to perform a given task. There is several training algorithm that can be used to compute a neural network problem. Concrete is a composite construction material composed of cement (commonly Portland cement) and other cementitious materials such as fly ash and slag cement, aggregate (generally a coarse aggregate made of gravels or crushed rocks such as limestone, or granite, plus a fine aggregate such as sand), water, and chemical admixtures. This paper presents the analysis of Backpropagation Neural Network Training Algorithms in Artificial Neural Network (ANN) using MATLAB and demonstrates the analysis of training algorithms using the dataset of concrete compressive strength.