| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4942914 | Expert Systems with Applications | 2018 | 9 Pages |
â¢The optimal value of fuzzfiers is necessary for better management of uncertainty.â¢Previously finding appropriate value of fuzzfiers is hard task for data sets.â¢I adaptively calculate optimal fuzzifiers for interval type-2 fuzzy algorithms.â¢Adaptively calculated fuzzifier values have been proven optimal.
Type-2 fuzzy sets are preferred over type-1 sets as they are capable of addressing uncertainty more efficiently. Fuzzifier values play a pivotal role in managing these uncertainties; still selecting an appropriate value of fuzzifier has been a tedious task. Generally, based on observation, a particular value of fuzzifier is chosen from a given range of values for a given dataset. In this paper, I have tried to adaptively compute suitable fuzzifier values of interval type-2 fuzzy c-means for a given pattern. Information is extracted from individual data points using histogram approach and this information is further processed to give us the two fuzzifier values m1 and m2. These obtained values are bounded within some upper and lower bounds based on existing methods.
