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Predictions of USA Presidential Parties From 2021 to 2037 Using Historical Data Through Square Wave-Activated WASD Neural Network

Authors :
Tianyu Zeng
Yunong Zhang
Zhenyu Li
Binbin Qiu
Chengxu Ye
Source :
IEEE Access, Vol 8, Pp 56630-56640 (2020)
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

The United States of America (USA) has been the most powerful country in the world for decades. The country's global leadership and power are critical for international security and intercountry relationships, in which the political events yield differences. With the deepening development of globalization and the recovery of the global economy, the future policies and presidential parties of the USA should be predicted to avoid potential crises. However, making unbiased and robust predictions is challenging. This study presents a weight-and-structure-determination (WASD) algorithm-based feedforward neural network activated by a set of square wave functions to predict the presidential parties in the USA from 2021 to 2037. The historical data of presidential parties from 1853 to 2017 are used as the training set in the numerical experiments, and the results show that the proposed neural network can predict 9 terms of future presidential parties. Compared with other models, the proposed model is more efficient, robust, purely time-driven, and bias-independent. Through the square wave-activated WASD neural network, the Democratic Party is predicted to win in 2025, 2029, and 2033, whereas the Republican Party might win in 2021 and 2037.

Details

Language :
English
ISSN :
21693536
Volume :
8
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.3be62ca345a449daa33ce6a51147b708
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2020.2982192