3 research outputs found
Some applications of possibilistic mean value, variance, covariance and correlation
In 2001 we introduced the notions of possibilistic mean value and variance of fuzzy numbers. In this paper we list some works that use these notions. We shall mention some application areas as wel
Probabilistic clustering algorithms for fuzzy rules decomposition
The fuzzy c-means (FCM) clustering algorithm is the best known and used
method in fuzzy clustering and is generally applied to well defined set of data. In this
paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied
to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one
for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to
the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be
conveniently linked into a Parallel Collaborative Structures
Clustering algorithms for fuzzy rules decomposition
This paper presents the development, testing
and evaluation of generalized Possibilistic
fuzzy c-means (FCM) algorithms applied to
fuzzy sets. Clustering is formulated as a
constrained minimization problem, whose
solution depends on the constraints imposed
on the membership function of the cluster and
on the relevance measure of the fuzzy rules.
This fuzzy clustering of fuzzy rules leads to a
fuzzy partition of the fuzzy rules, one for each
cluster, which corresponds to a new set of
fuzzy sub-systems. When applied to the
clustering of a flat fuzzy system results a set
of decomposed sub-systems that will be
conveniently linked into a Hierarchical
Prioritized Structures