A CRITICAL REVIEW OF FUZZY INFERENCE SYSTEMS FOR DECISION SUPPORT APPLICATIONS WITH SPECIAL REFERENCE TO THE MAMDANI MODEL

Ankit Kumar; Sanjay Bhadoriya

doi:10.29121/ijoest.v6.i6.2022.754

Authors

Ankit Kumar Student, Department of Computer Technology Application, Dr. A.P.J. Abdul Kalam University, Indore, India
Dr. Sanjay Bhadoriya Professor, Department of Computer Technology Application, Dr. A.P.J. Abdul Kalam University, Indore, India

DOI:

https://doi.org/10.29121/ijoest.v6.i6.2022.754

Keywords:

MFIS, Fuzzy Logic, Linguistic Variables, Defuzzification, Fuzzification

Abstract

Fuzzy inference systems (FIS) have been found to be very useful tools for decision making in uncertain and complex environments. One major type of FIS, which have earned wide attention, is the Mamdani fuzzy inference system (MFIS), which tries to capture and implement expert knowledge in terms of linguistic variables as well fuzzy rules. This review processes to presents a detailed study on the use of MFIS for decision making. The review starts with an introduction to the basic concepts of fuzzy logic and an MFIS structure involving linguistic variables, different types of membership functions, fuzzy rule bases and defuzzification methods.
It examines the fundamental ideas and methods of operation of the MFIS, emphasizing its capacity to manage imprecise and ambiguous data and simulate human-like thinking. Additionally, the paper looks at the many ways that MFIS is used in decision-making in a variety of fields, including engineering, healthcare, finance, and environmental management. It talks about how MFIS is used to create intelligent decision support systems by modeling and reasoning with ambiguous and subjective data. The review addresses the benefits and drawbacks of MFIS in decision making, along with application examples. It highlights how the MFIS's linguistic variables and fuzzy rules can be interpreted, making the decision-making process more transparent and intelligible. Nonetheless, issues with model interpretability, computational complexity, and rule base design are also covered.

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