Fuzzy rule‐based inference in system dynamics formulations

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

Handling Four DOF Robot to Move Objects Based on Color and Weight using Fuzzy Logic Control

“…Rule Base contains a set of fuzzy rules that serve to determine decisions, consisting of IF-THEN statements in fuzzy logic format. The statements in these fuzzy rules use connectors such as AND, OR, or NOT, and the degree of truth is calculated for the predicate set [59], [60].…”

“…3. A fuzzy inference engine is one of the core components in a fuzzy logic system that is responsible for making decisions based on the rules defined in the fuzzy rule base [60], [61].…”