Fuzzy Uncertain Information Research Articles

A general framework for multi-granulation rough decision-making method under q-rung dual hesitant fuzzy environment

In the realistic decision-making (DM) process, the DM results were provided by multiple DM experts, which are more accurate than those based on one DM expert. Therefore, the multi-granulation rough set (MGRS) model is more accurate in DM problems. It is imperative to apply the idea of multi-granulation to the complex fuzzy uncertain information. By combining q-rung dual hesitant fuzzy sets (q-DHFSs) with multi-granulation rough sets (MGRSs) over two universes, we propose a q-rung dual hesitant fuzzy multi-granulation rough set (q-RDHFMGRS) over two universes, and prove some of their basic properties. Then, based on this model, we propose a new multi-attribute DM algorithm. Finally, we validate the practicability and validity of the algorithm through an example of medical diagnosis.

Modeling and Simulating of Uncertain Quality Abnormity Diagnosis

There is much fuzzy uncertain information during the diagnosis of quality abnormity. The effective utilization model of that can provide important decision-making support. In this study, we consider three main types of fuzzy production rules, which can be used in fuzzy quality abnormity diagnosis problem and their presentation models are constructed by use of Fuzzy Reasoning Petri Nets (FRPNs). Considering of the graphic representation and logic structure of FRPNs, we propose the method for simulating model using Matlab toolbox state flow. By establishing a corresponding relationship between FRPNs rules and state flow block diagram, three simulating models for the three corresponding FRPNs' basic structure are developed. Finally, we give an application case of the proposed model. Taking place truth degree data of FRPNs as input, the diagnosis process and results can be shown dynamically in the state flow simulating model under Matlab environment. The result illustrated that the method proposed can give reliable information for process maintenance and abnormal causes' location.