Comparative analysis of various artificial neural network learning algorithms

An artificial neural network is a methodology for developing an intelligent system that can imitate the functioning of the human brain. New algorithms are increasingly being explored to improve efficiency and accuracy. Thus, this article examines the effectiveness and feasibility of using various algorithms for training an artificial neural network in the backpropagation architecture. The learning algorithms are developed and implemented in the MATLAB environment. Various learning algorithms are analyzed to determine the accuracy of results. The analysis is performed using a house price forecasting dataset from Kaggle.