Fuzzy relation equations are commonly utilized to describe the fuzzy relationship between
the antecedent and the consequent parts of complex data environment, and play a vital role …

Developing and optimizing fuzzy relation equations are of great relevance in system
modeling, which involves analysis of numerous fuzzy rules. As each rule varies with respect …

Building abstract concepts is essential to humans when acquiring knowledge, realizing
processing (reasoning), and communicating findings. Abstraction comes hand in hand with …

In the real world, phenomena are often observed and recorded by multiple organizations
which results in multiple sources of data. When dealing with such data, the centralized …

We propose to accommodate herein our novel unified granular framework that uses a
developed hybrid fuzzy-neuro relational system in order to tackle a complex medical …

Fuzzy relational equations play an important role as a platform in various applications of
fuzzy sets and systems. The resolution and optimization of fuzzy relational equations are of …

This study is concerned with fuzzy relation equations with continuous t-norms in the form
ATR= B, where A and B are the fuzzy subsets of X and Y, respectively; R⊂ X× Y is a fuzzy …

Lately, the sup-t-norm composition of fuzzy relations has been used instead of the well-
known max–min. Thus, there is a need for methods of studying and solving sup-t-norm fuzzy …

A knowledge distillation method is proposed that uses inverse inference to deploy a
granular-fuzzy classifier on devices with limited computing resources. The System of Fuzzy …

In recent years, granular fuzzy models have become an intensively studied category of fuzzy
models. Granular fuzzy models help elevate the existing models to the higher level of …