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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Bibliographic Details
Published inSystem dynamics review Vol. 35; no. 4; pp. 310 - 336
Main Authors Sabounchi, Nasim S., Triantis, Konstantinos P., Kianmehr, Hamed, Sarangi, Sudipta
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Ltd 01.10.2019
Wiley Periodicals Inc
Subjects
Dynamic models
Fuzzy logic
Fuzzy systems
Inference
Modelling
Risk
Substructures
System dynamics
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ISSN0883-7066
1099-1727
DOI10.1002/sdr.1644

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Summary: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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ISSN:0883-7066
1099-1727
DOI:10.1002/sdr.1644