Centroid of a type-2 fuzzy set
Information Sciences: an International Journal
An efficient centroid type-reduction strategy for general type-2 fuzzy logic system
Information Sciences: an International Journal
Efficient triangular type-2 fuzzy logic systems
International Journal of Approximate Reasoning
The collapsing method of defuzzification for discretised interval type-2 fuzzy sets
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks
Information Sciences: an International Journal
Information Sciences: an International Journal
Enhanced Karnik-Mendel algorithms
IEEE Transactions on Fuzzy Systems
Type-reduction of the discretised interval type-2 fuzzy set
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Interval type-2 fuzzy logic congestion control for video streaming across IP networks
IEEE Transactions on Fuzzy Systems
α-plane representation for type-2 fuzzy sets: theory and applications
IEEE Transactions on Fuzzy Systems
Toward general type-2 fuzzy logic systems based on zSlices
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Interval Type-2 fuzzy voter design for fault tolerant systems
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Geometric Type-1 and Type-2 Fuzzy Logic Systems
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A closed form type reduction method for piecewise linear interval type-2 fuzzy sets
International Journal of Approximate Reasoning
Fixed charge transportation problem with type-2 fuzzy variables
Information Sciences: an International Journal
Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts
Knowledge-Based Systems
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For generalised type-2 fuzzy sets the defuzzification process has historically been slow and inefficient. This has hampered the development of type-2 Fuzzy Inferencing Systems for real applications and therefore no advantage has been taken of the ability of type-2 fuzzy sets to model higher levels of uncertainty. The research reported here provides a novel approach for improving the speed of defuzzification for discretised generalised type-2 fuzzy sets. The traditional type-reduction method requires every embedded type-2 fuzzy set to be processed. The high level of redundancy in the huge number of embedded sets inspired the development of our sampling method which randomly samples the embedded sets and processes only the sample. The paper presents detailed experimental results for defuzzification of constructed sets of known defuzzified value. The sampling defuzzifier is compared on aggregated type-2 fuzzy sets resulting from the inferencing stage of a FIS, in terms of accuracy and speed, with other methods including the exhaustive and techniques based on the @a-planes representation. The results indicate that by taking only a sample of the embedded sets we are able to dramatically reduce the time taken to process a type-2 fuzzy set with very little loss in accuracy.