Fuzzy rule-based inference in system dynamics formulations

被引:2
|
作者
Sabounchi, Nasim S. [1 ]
Triantis, Konstantinos P. [2 ]
Kianmehr, Hamed [3 ]
Sarangi, Sudipta [4 ]
机构
[1] CUNY, Dept Hlth Policy & Management, Ctr Syst & Community Design, Grad Sch Publ Hlth & Hlth Policy, 55 W 125 St,7th Floor, New York, NY 10027 USA
[2] Virginia Tech, Grado Dept Ind & Syst Engn, Falls Church, VA 22043 USA
[3] Univ Florida, Dept Pharmaceut Outcomes & Policy, Coll Pharm, Gainesville, FL 32610 USA
[4] Virginia Tech, Dept Econ, Blacksburg, VA 24061 USA
关键词
T-NORMS; SIMULATION; POLICY; LOGIC;
D O I
10.1002/sdr.1644
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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 (c) 2020 System Dynamics Society
引用
收藏
页码:310 / 336
页数:27
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