Interval Type-2 Fuzzy C Clustering for Detecting Spherical Shells
نویسندگان
چکیده
منابع مشابه
Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...
متن کاملInterval Type-2 Fuzzy Rough Sets and Interval Type-2 Fuzzy Closure Spaces
The purpose of the present work is to establish a one-to-one correspondence between the family of interval type-2 fuzzy reflexive/tolerance approximation spaces and the family of interval type-2 fuzzy closure spaces.
متن کاملInterval Type-2 Fuzzy Neural System Based Control with Recursive Fuzzy C-Means Clustering
This paper focuses on the design of a novel control approach. Its contribution to the existing literature is that in the design of an interval type-2 fuzzy neural system, recursive fuzzy c-means clustering algorithm is used and the designed algorithm is applied in control applications. The center and the standard deviation values of the interval type-2 Gaussian membership functions at the antec...
متن کاملSuccessive Optimization of Interval Type-2 Fuzzy C-Means Clustering Algorithm-based Fuzzy Inference Systems
A design methodology of interval type-2 fuzzy c-means clustering algorithm-based fuzzy inference systems (IT2FCMFIS) is introduced in this paper. An interval type-2 fuzzy c-means (IT2FCM) clustering algorithm is developed to generate the fuzzy rules in the form of the scatter partition of input space. And the individual partitioned spaces describe the fuzzy rules equal to the number of clusters...
متن کاملimage segmentation: type–2 fuzzy possibilistic c-mean clustering approach
image segmentation is an essential issue in image description and classification. currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. moreover, there are many uncertainties and vagueness in images, which crisp clustering and even type-1 fuzzy clustering could not handle. hence, type-...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Korean Institute of Intelligent Systems
سال: 2004
ISSN: 1976-9172
DOI: 10.5391/jkiis.2004.14.6.713