Analyzing the Performance of the Fuzzy Inference System in Decision Making

Abstract

Inference systems that are fuzzy, It is common practise to make use of models such as the Mamdani and Sugeno models in order to take into consideration the presence of uncertainty and imprecision in the decision-making process. MATLAB is a well-known programming environment that provides persons who are interested in developing and implementing fuzzy inference systems with the necessary tools and strategies to accomplish their goals. In order to evaluate Diabetes Mellitus (DM), the Mamdani and Sugeno fuzzy inference systems have been developed in MATLAB. This abstract provides a brief summary of how the evaluation was carried out.The Mamdani model provides a description of uncertain data through the utilisation of fuzzy sets and is founded on language standards. Through the utilisation of the Fuzzy Logic Toolbox, users of MATLAB are able to rapidly construct and simulate Mamdani fuzzy systems. Membership functions, fuzzy rule sets, simulations, and Mamdani system optimisations can all be defined and created by users without any restrictions that are placed on them. The visualisation options that are available in MATLAB, such as the surface plot and the rules plot, help to make the behaviour of the system more understandable.For the purpose of producing inferences and predictions, the Sugeno model, also known as the Takagi-Sugeno-Kang (TSK) model, combines fuzzy principles with linear calculations. It is possible to implement Sugeno fuzzy systems by utilising the Fuzzy Logic Toolbox that is included in MATLAB. The user is able to create the linear functions that are associated to each rule after the information regarding the input-output relationships has been specified through the utilisation of linguistic variables and membership functions. Evaluation, simulation, and visualisation of the rule surfaces and output response curves of Sugeno fuzzy systems can be accomplished in MATLAB in a short amount of time. To summarise, the Mamdani and Sugeno fuzzy inference systems are capable of being constructed in an efficient manner by utilising MATLAB. There is software available for rapid system modelling, simulation, and analysis applications. The techniques of fuzzy logic that are available in MATLAB can be utilised by both professionals and academics in order to address the issue of uncertainty and imprecision in decision-making processes.