@article{HU2022, 
author = {Hongping HU and Shichang QIAO and Huihua KONG and Qiaowang XU and Yanping BAI},
title = {Prediction of COVID-19 Based on the Weighted Average Salp Swarm Algorithm and BP Neural Network},
year = {2022},
journal = {Journal of Xinjiang University(Natural Science Edition in Chinese and English)},
volume = {39},
number = {1},
pages = {19-25},
keywords = {Corona Virus Disease 2019, salp swarm algorithm, BP neural network, function optimization, prediction of COVID-19},
url = {https://www.sciopen.com/article/10.13568/j.cnki.651094.651316.2021.03.30.0001},
doi = {10.13568/j.cnki.651094.651316.2021.03.30.0001},
abstract = {Corona Virus Disease 2019(COVID-19) is one of the global concerns due to its highly infectious and highly pathogenic coronavirus. It is of great value to effectively predict the cumulative number of confirmed cases of COVID-19 for the prevention and control of COVID-19. In this paper, the weighted average salp swarm algorithm is proposed, named by AVSSA, whose validation is performed by 23 benchmark functions. Then AVSSA is utilized to optimize the parameters of BP neural network to establish the predicted model AVSSA-BP for predicting the COVID-19. The experimental results show that the predicted model AVSSA-BP has the least errors and the highest coefficient of determination. Therefore, the proposed AVSSA is an effective algorithm.}
}