Artificial Neural Networks are connectionist systems formed by numerous process units called neurons connected to each other, which adapt their structure through learning techniques to solve problems of function approximation and pattern classification. They process information that is supplied to them, either to obtain relationships between them and the objective function that is intended to be approximated, or by classifying these data into different categories. Regression analysis aims to determine the type of functional relationship that exists between a dependent variable and one or more independent variables. The purpose of the research is to use regression methods (multiple regression) and artificial neural networks (multilayer perceptron) to determine the influence of spending execution on the regional government's public budget. 95% of the variability of the budget of Moquegua region has been determined and explained by the three sectors (primary, secondary and tertiary) and 5% is determined by other factors outside the regional government budget. The determination coefficients R 2 = 95.9% in the regression model and R 2 = 95.3% in the neural network (multilayer perceptron). It has been demonstrated that Artificial neural networks and regression models have obtained very similar results, achieving good and good-fit models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.