Type-n fuzzy logic - the next level of type-1 and type-2 fuzzy logic
Saikat Maity, Sanjay Chakraborty, Saroj Kumar Pandey, Indrajit De, Sourasish Nath · International Journal of Intelligent Engineering Informatics · 2023
The level of uncertainty in a system can be reduced by using type-2 fuzzy logic, which has a superior ability to handle linguistic uncertainties by modelling ambiguity and unreliability of information. Unfortunately, type-2 fuzzy sets are harder to use and understand than type-1 fuzzy sets. This article provides a comprehensive idea on non-stationary fuzzy inference system (FIS) as well as a generalised approach to the extended type-2 fuzzy approach. A new proposed breakdown of T2FS along with stationary and non-stationary fuzzy sets (FIS) is also described using the fuzzy inference system in this article. Besides that, it describes a new generalised FIS technique and ends with a generalised computation of the centroid of a type-2 fuzzy system. A new proposed breakdown of T2FS along with stationary and non-stationary fuzzy sets (FIS) is also described using the fuzzy inference system in this article.