scholarly journals Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1548
Author(s):  
Marjana Čubranić-Dobrodolac ◽  
Libor Švadlenka ◽  
Svetlana Čičević ◽  
Aleksandar Trifunović ◽  
Momčilo Dobrodolac
Keyword(s):  
Traffic Accidents ◽  
Driving Simulator ◽  
Fuzzy Inference ◽  
Road Traffic ◽  
Space Perception ◽  
Road Traffic Accidents ◽  
Inference System ◽  
Interval Type ◽  
The Impact

A constantly increasing number of deaths on roads forces analysts to search for models that predict the driver’s propensity for road traffic accidents (RTAs). This paper aims to examine a relationship between the speed and space assessment capabilities of drivers in terms of their association with the occurrence of RTAs. The method used for this purpose is based on the implementation of the interval Type-2 Fuzzy Inference System (T2FIS). The inputs to the first T2FIS relate to the speed assessment capabilities of drivers. These capabilities were measured in the experiment with 178 young drivers, with test speeds of 30, 50, and 70 km/h. The participants assessed the aforementioned speed values from four different observation positions in the driving simulator. On the other hand, the inputs of the second T2FIS are space assessment capabilities. The same group of drivers took two types of space assessment tests—2D and 3D. The third considered T2FIS sublimates of all previously mentioned inputs in one model. The output in all three T2FIS structures is the number of RTAs experienced by a driver. By testing three proposed T2FISs on the empirical data, the result of the research indicates that the space assessment characteristics better explain participation in RTAs compared to the speed assessment capabilities. The results obtained are further confirmed by implementing a multiple regression analysis.

Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
İlker Gölcük
Keyword(s):  
Failure Modes ◽  
Fuzzy Inference ◽  
Real Life ◽  
Effect Analysis ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
Interval Type

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

Interval Type 2 Fuzzy Fireworks Algorithm for Clustering

2020 ◽  
pp. 195-211
Author(s):  
Juan Barraza ◽  
Fevrier Valdez ◽  
Patricia Melin ◽  
Claudia I. Gonzalez
Keyword(s):  
Fuzzy Inference ◽  
Optimization Method ◽  
Optimal Number ◽  
Dynamic Adjustment ◽  
Number Of Clusters ◽  
Inference System ◽  
Fireworks Algorithm ◽  
Interval Type ◽  
Optimal Number Of Clusters

This chapter presents Interval Type 2 Fuzzy Fireworks Algorithm for clustering (IT2FWAC). It is an optimization method for finding the optimal number of clusters based on the centroid features which uses the Fireworks Algorithm (FWA), but with a dynamic adjustment of parameters using an Interval Type 2 Fuzzy Inference System (IT2FIS). Three variations of the IT2FWAC are proposed to find the optimal number of clusters for different datasets: IT2FWAC -I, IT2FWAC -II, and IT2FWAC –III. They are explained in detail.

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