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Modelling dynamics of Coronavirus disease 2019 spread for pandemic forecasting based on simulink.
Physical Biology ( IF 2 ) Pub Date : 2021-04-19 , DOI: 10.1088/1478-3975/abf990
Xian-Xian Liu 1 , Shimin Hu 1 , Simon James Fong 1 , Rubén González Crespo 2 , Enrique Herrera-Viedma 3
Affiliation  

In this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The Susceptible-Exposed-Asymptomatic but infectious-symptomatic and Infectious (Severe Infected Population + Mild Infected Population)-Recovered-Deceased (SEAI(I1+I2)RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.

中文翻译:

基于Simulink的2019年冠状病毒疾病传播动力学建模,用于大流行预测。

在本文中,我们演示了MATLAB在基于Simulink开发大流行预测系统中的应用。无监督学习和两种监督学习的易感暴露无症状但有感染症状和传染性(重度感染人口+轻度感染人口)-恢复-衰落(SEAI(I1 + I2)RD)物理模型-integral-derivative(PID)和小波神经网络PID学习用于建立预测控制系统模型,该模型可实现基于自学习人工智能(AI)的控制。设置参数后,将预测进入模型的数据,并计算出将来数据集的值。添加PID控制器以确保系统在迭代学习开始时不会发散。
更新日期:2021-04-19
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