Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019.
ISA Trans
; 124: 57-68, 2022 May.
Article
in English
| MEDLINE | ID: covidwho-1778222
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 1.24×10-5, MAE of 2.62×10-6, R2 of 0.99976, and MAPE of 6.33×10-6.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Fuzzy Logic
/
COVID-19
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
ISA Trans
Year:
2022
Document Type:
Article
Affiliation country:
J.isatra.2022.03.031
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