Asynchronous state estimation for switched nonlinear reaction–diffusion SIR epidemic models with impulsive effects

Abstract

This paper focuses on the asynchronous interval type-2 fuzzy state estimation for switched nonlinear reaction–diffusion susceptible–infected–recovered (SIR) epidemic models with impulsive effects. Initially, based on the stage characteristics of epidemic outbreaks, impulsive switched reaction–diffusion neural networks are proposed to model SIR epidemics more comprehensively. Then, the investigated models are linearized by using the interval type-2 Takagi–Sugeno fuzzy method, which can handle the nonlinearity and uncertainty of the system well. Next, considering the phenomenon of asynchronous switching between the system state and the estimator one due to system identification and other factors, the asynchronous fuzzy state estimator with switching and impulsive features is designed to accurately estimate the state of the target systems. Finally, sufficient conditions for ensuring the state estimation error to be stable are derived, and the effectiveness of the theoretical results is validated by numerical examples.