1. Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019.
- Author
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dos Santos Gomes, Daiana Caroline and de Oliveira Serra, Ginalber Luiz
- Subjects
SARS-CoV-2 ,FORECASTING ,FUZZY systems ,DEATH rate ,FUZZY algorithms - Abstract
This paper presents a computational model based on interval type-2 fuzzy systems for analysis and forecasting of COVID-19 dynamic spreading behavior. The proposed methodology is related to interval type-2 fuzzy Kalman filters design from experimental data of daily deaths reports. Initially, a recursive spectral decomposition is performed on the experimental dataset to extract relevant unobservable components for parametric estimation of the interval type-2 fuzzy Kalman filter. The antecedent propositions of fuzzy rules are obtained by formulating a type-2 fuzzy clustering algorithm. The state space submodels and the interval Kalman gains in consequent propositions of fuzzy rules are recursively updated by a proposed interval type-2 fuzzy Observer/Kalman Filter Identification (OKID) algorithm, taking into account the unobservable components obtained by recursive spectral decomposition of epidemiological experimental data of COVID-19. For validation purposes, through a comparative analysis with relevant references of literature, the proposed methodology is evaluated from the adaptive tracking and forecasting of COVID-19 dynamic spreading behavior, in Brazil, with the better results for RMSE of 1. 24 × 1 0 − 5 , MAE of 2. 62 × 1 0 − 6 , R 2 of 0.99976, and MAPE of 6. 33 × 1 0 − 6 . [ABSTRACT FROM AUTHOR]
- Published
- 2022
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