Abstract
The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts’ knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.
Highlights
Fuzzy inference systems are based on fuzzy IF- rules that connect the fuzzy input and output variables
The proposed IT-2 fuzzy inference systems (FIS) adopts the parameterized Yager-generating function to determine the degrees of hesitation in Type-2 fuzzy set, and optimal target values based on particle swarm optimization
The proposed IT-2 FIS is capable of dealing with complex capacity loading and medical diagnosis problems in which various uncertain variables and incomplete knowledge are involved
Summary
Fuzzy inference systems are based on fuzzy IF- rules that connect the fuzzy input and output variables. A fuzzy inference system (FIS) can be used as a prediction model that inputs or outputs data with a high uncertainty. Blanes-Vidal et al [4] used neuro-fuzzy inference systems (NFIS) to develop a novel approach for exposure assessment, in which the inputs of the model were obtainable proximity measures and the output was the residential exposure to air pollutants. The input variables, including PM2.5 , PM10 , and total suspended particles (TSP), as well as the health-risk level, as the output variable, were fuzzed by using a fuzzy inference system. This method could be used effectively in other workplaces, such as hospitals and health-care facilities. A summary of the research that has investigated FIS as a classification technique since 2016 is shown in Table 1, illustrating that FIS has been widely applied in various fields and that combining FIS with other techniques usually results in a better performance
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