Laser simulator logic: A novel inference system for highly overlapping of linguistic variable in membership functions

A new approach for Fuzzification and Defuzzification processes of a high degree of overlapping between the linguistic variables through proportional and relative-dynamic-scale membership functions is pre-sented in this paper. Most of related works that utilized type-1 and 2 fuzzy logic algorithms are confused and questionable from two points of view: (1) the overlapping between linguistic variables values starts almost from the midst of each other; so, what will be happened if it starts before the midst i.e., increasing the degree of overlapping, -(2) the linguistic variables are commonly represented by symmetrical mem-bership functions form. It has been found that the current type 1 and 2 fuzzy logic are no longer suitable to deal with highly inference between linguistic variables. Thus, a novel algorithm, called laser simulator logic, is introduced in this paper to deal with the issue of highly inference of linguistic variables member-ship functions, which depends mainly on a proportional and relative-dynamic scale of membership func-tion that will change the scale of membership continuously based on the crisp input/output. Results show that the laser simulator performs better than both type-1 and 2 fuzzy logic when there is a high degree of overlapping between the linguistic variables.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper presents a new approach to handle the fuzzification and defuzzification processes of linguistic variables with a high degree of overlapping. It introduces proportional and relative-dynamic-scale membership functions and proposes a novel algorithm called laser simulator logic. The results demonstrate that the laser simulator outperforms traditional type-1 and type-2 fuzzy logic when dealing with highly overlapped linguistic variables.