人工神经网络
反向传播
非线性系统
计算机科学
均方误差
颂歌
流行病模型
接种疫苗
人口
人工智能
统计
数学
应用数学
医学
免疫学
环境卫生
物理
量子力学
作者
Muhammad Shoaib,Nabeela Anwar,Iftikhar Ahmad,Shafaq Naz,Adiqa Kausar Kiani,Muhammad Asif Zahoor Raja
出处
期刊:International Journal of Modern Physics B
[World Scientific]
日期:2022-06-28
卷期号:36 (18)
被引量:10
标识
DOI:10.1142/s0217979222501004
摘要
This paper portrays the exploitation/exploration of artificial intelligence (AI) inspired computing to study the behavior of the multi-delay differential systems that revealed the impact of latent period and the dynamics of the a susceptible, vaccinated, exposed, infectious and recovered (SVEIR) epidemic model involving vaccination by means of the neural networks backpropagation with Levenberg–Marquardt scheme (NNs-BLMS). The reference solutions of the five classes ordinary differential equations (ODEs) model of SVEIR dynamics are calculated by applying the Adams method for variation in delay due to the time spent in preventing the infection and delay due to the duration of recovery immunity in the cured population. The designed NNs-BLMS used the created dataset arbitrarily for training, validation, as well as, testing samples to determine the estimated results of the nonlinear SVEIR epidemic model involving vaccination impact. The achieved accuracy of the designed NNs-BLMS is authenticated/proven by analyzing the fitness function based on mean square error (MSE), regression analysis, and error histogram for sundry scenarios of SVEIR epidemic system with the impact of vaccination.
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