Fuzzy rule‐based inference in system dynamics formulations
In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, includ...
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| Published in | System dynamics review Vol. 35; no. 4; pp. 310 - 336 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.10.2019
Wiley Periodicals Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0883-7066 1099-1727 |
| DOI | 10.1002/sdr.1644 |
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| Abstract | In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as “low‐risk,” “average‐risk” or “high‐risk.” We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context.
Copyright © 2020 System Dynamics Society |
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| AbstractList | In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as “low‐risk,” “average‐risk” or “high‐risk.” We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context.Copyright © 2020 System Dynamics Society In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as “low‐risk,” “average‐risk” or “high‐risk.” We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context. Copyright © 2020 System Dynamics Society |
| Author | Sarangi, Sudipta Kianmehr, Hamed Sabounchi, Nasim S. Triantis, Konstantinos P. |
| Author_xml | – sequence: 1 givenname: Nasim S. orcidid: 0000-0002-8027-8779 surname: Sabounchi fullname: Sabounchi, Nasim S. email: nasim.sabounchi@sph.cuny.edu organization: City University of New York (CUNY) Graduate School of Public Health and Health Policy – sequence: 2 givenname: Konstantinos P. surname: Triantis fullname: Triantis, Konstantinos P. organization: Virginia Tech – sequence: 3 givenname: Hamed surname: Kianmehr fullname: Kianmehr, Hamed organization: College of Pharmacy, University of Florida, Gainesville – sequence: 4 givenname: Sudipta surname: Sarangi fullname: Sarangi, Sudipta organization: Virginia Tech |
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| Title | Fuzzy rule‐based inference in system dynamics formulations |
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