As an integral part of interval type-2 fuzzy logic system (IT2FLS), type reduction (TR) plays a vital role in determining the performance of IT2FLS. Out of many type reduction algorithms, only Karnik-Mendel type TR algorithms capture the essence of interval type-2 fuzzy sets in type reduction. Enhanced Karnik-Mendel (EKM) algorithm is the most commonly used TR algorithm. In this work, we propose three new initializations for EKM algorithm. It is shown they are performing better than EKM and one of the proposed initializations significantly outperforms others. The performance gain can be upto 40% as per comprehensive simulation results demonstrated in this paper. Our findings are justified by computational time savings and iteration requirem...
Abstract—To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS...
Abstract—This paper provides an answer to the question that the type-2 fuzzy logic community is now ...
10.1109/FUZZY.2008.4630559IEEE International Conference on Fuzzy Systems1425-1432PIFS
Interval Type-2 fuzzy systems allow the possibility of considering uncertainty in models based on fu...
Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic syst...
Interval type-2 fuzzy logic controllers have demonstrated better abilities to handle uncertainties t...
Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for th...
Type reduction (TR) is one of the key components of interval type-2 fuzzy logic systems (IT2FLSs). M...
Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many appl...
Improving the efficiency of type-reduction algorithms continues to attract research interest. Recent...
Abstract—The Karnik–Mendel (KM) algorithms are iterative procedures widely used in fuzzy logic theor...
[[abstract]]Type reduction of interval type-2 (IT2) fuzzy sets is essential in conducting the type-2...
In this paper, we propose a novel interval type-2 (IT2) fuzzy clustering algorithm by incorporating ...
Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in litera...
Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when...
Abstract—To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS...
Abstract—This paper provides an answer to the question that the type-2 fuzzy logic community is now ...
10.1109/FUZZY.2008.4630559IEEE International Conference on Fuzzy Systems1425-1432PIFS
Interval Type-2 fuzzy systems allow the possibility of considering uncertainty in models based on fu...
Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic syst...
Interval type-2 fuzzy logic controllers have demonstrated better abilities to handle uncertainties t...
Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for th...
Type reduction (TR) is one of the key components of interval type-2 fuzzy logic systems (IT2FLSs). M...
Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many appl...
Improving the efficiency of type-reduction algorithms continues to attract research interest. Recent...
Abstract—The Karnik–Mendel (KM) algorithms are iterative procedures widely used in fuzzy logic theor...
[[abstract]]Type reduction of interval type-2 (IT2) fuzzy sets is essential in conducting the type-2...
In this paper, we propose a novel interval type-2 (IT2) fuzzy clustering algorithm by incorporating ...
Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in litera...
Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when...
Abstract—To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS...
Abstract—This paper provides an answer to the question that the type-2 fuzzy logic community is now ...
10.1109/FUZZY.2008.4630559IEEE International Conference on Fuzzy Systems1425-1432PIFS