Abstract
Fuzzy Systems are the managers for the modeling environment uncertainty for real time decision making. Type 1 fuzzy systems are much interpretable but less accurate than the type 2 and Interval Type 2 Fuzzy Systems (IT2FS). The paper introduces an experimental analysis to address the interpretability quantification and accuracy measurement in all types of fuzzy implementations. The experiment is carried out on the Thyroid dataset which leads to predict the level of Thyroid in the patients.
Highlights
Fuzzy systems [1] have extraordinary capability to model linguistic computation [2]
This paper introduces new findings for the application of interval type 2 fuzzy system (IT2FS) and Type-2 Fuzzy Systems (T2FS) in place of simple fuzzy systems for improving the system accuracy
T1FS have the capability to deal with the models consisting of lot of uncertainty in the development and working environment
Summary
Membership functions which are based on mathematical formulation of linguistic values are the core part of any fuzzy system. To get more precision, this membership degree can be further represented by a new fuzzy set. Such systems are called Type-2 Fuzzy Systems (T2FS) [3, 4]. Sometimes the computation cost of the fuzzy systems (type 2) is high, so an alternative is proposed to replace new fuzzy set with an interval 0 and 1 [4] interval type 2 fuzzy system (IT2FS). This paper introduces new findings for the application of IT2FS and T2FS in place of simple fuzzy systems for improving the system accuracy.
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