Interactive Fuzzy Process: An Epidemiological Model

In this study we analyze an two-dimensional epidemiological model via fuzzy differential equation considering that the solution is an interactive fuzzy process. More particularly, we will consider the case where this process is linearly correlated.

[1]  Hung T. Nguyen,et al.  A First Course in Fuzzy Logic , 1996 .

[2]  B. Bede,et al.  Fuzzy Differential Equations in Various Approaches , 2015 .

[3]  Laécio C. Barros,et al.  Fuzzy differential equations with interactive derivative , 2017, Fuzzy Sets Syst..

[4]  Osmo Kaleva.  Fuzzy differential equations , 1987 .

[5]  Barnabás Bede,et al.  Mathematics of Fuzzy Sets and Fuzzy Logic , 2012, Studies in Fuzziness and Soft Computing.

[6]  Christer Carlsson,et al.  Additions of completely correlated fuzzy numbers , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[7]  Laécio C. Barros,et al.  A First Course in Fuzzy Logic, Fuzzy Dynamical Systems, and Biomathematics - Theory and Applications , 2016, Studies in Fuzziness and Soft Computing.

[8]  Estevão Esmi,et al.  Population growth model via interactive fuzzy differential equation , 2019, Inf. Sci..

[9]  L. C. Barros,et al.  The SI epidemiological model with interactive fuzzy parameters , 2012, 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS).

[10]  Luciano Stefanini,et al.  Some notes on generalized Hukuhara differentiability of interval-valued functions and interval differential equations , 2012 .

[11]  Leah Edelstein-Keshet,et al.  Mathematical models in biology , 2005, Classics in applied mathematics.