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Fuzzy rule‐based inference in system dynamics formulations

Author

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  • Nasim S. Sabounchi
  • Konstantinos P. Triantis
  • Hamed Kianmehr
  • Sudipta Sarangi

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

Suggested Citation

  • Nasim S. Sabounchi & Konstantinos P. Triantis & Hamed Kianmehr & Sudipta Sarangi, 2019. "Fuzzy rule‐based inference in system dynamics formulations," System Dynamics Review, System Dynamics Society, vol. 35(4), pages 310-336, October.
    • Handle: RePEc:bla:sysdyn:v:35:y:2019:i:4:p:310-336
    DOI: 10.1002/sdr.1644
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