期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2022
卷号：52
期号：1
语种：English
出版社：Newswood and International Association of Engineers
摘要：Noise poses challenge to nonlinear Hammerstein?Wiener (HW) subsystem model application, because HW sub?system need large number of parameter interactions. How?ever, flexibility, soft computing, and automatic adjustment todynamic observation for best model fitting make it potentialfor forecasting nonlinear data. In this article, we adopted im?proved HW inference from Levenberg-Marquardt optimizationalgorithm to optimize HW subsystem and to select best modelparameters. Therefore, the adopted model is tested on COVID-19 confirmed reported cases, to estimate transmission rate ofCOVID-19 virus for period from 15th March 2020 to 29thApril 2020. Model validation is carried out on small dataset,which outperforms some existing models. The adopted modelis further evaluated using statistical metrics and reported bestaccuracy of 0.127 and 0.998 for Mean Absolute percentage error(MAPE) and coefficient of determination (R2) respectively,with best model complexity of 1.86. The obtained results arepromising enough in predicting spread of COVID-19 virus andmay inspire as guidance to relax lockdown restriction policies.
