Fuzzy C-mean (FCM) is the most well-known and widely-used fuzzy clustering algorithm. However, one of the weaknesses of the FCM is the way it assigns membership degrees to data which is based on the distance to the cluster centers. Unfortunately, the membership degrees are determined without considering the shape and density of the clusters. In this paper, we propose an algorithm which takes th...

Recently, the new operation ∆ was introduced over intuitionistic fuzzy sets and some of its properties were studied. Here, additional this operations are formulated checked, providing an analogue to De Morgan’s Law (Theorem 1), Fixed Point Theorem 2), connections between on one hand classical modal operators IFS Necessity Possibility, other (Theorems 3 4). It is shown that it can be used for a ...

Performance Appraisal is the systematic assessment of performance of employees to understand the abilities for further development. According to sixth pay commission the approach adopted for evaluation of teaching staff performance focuses on areas like Teaching, Learning, Extension, Research, Publication etc. which are actually fuzzy concepts that can be captured in fuzzy terms. In the propose...

Bipolar-valued fuzzification of the notion of implicative hyper BCK-ideals is considered. Using the concepts of positive and negative lever sets, a characterization of a bipolar fuzzy implicative hyper BCK-ideal is provided. A relation between a bipolar fuzzy hyper BCK-ideal and a bipolar fuzzy implicative hyper BCK-ideal is established. A condition for a bipolar fuzzy hyper BCK-ideal to be a b...

In this paper a Cellular Fuzzy Associative Memory containing fuzzy rules for gray image fuzzification is designed, considered as a subsystem of a CNN-based architecture able to store bidimensional patterns. After establishing the fuzzy rules which characterize the required FAM, these rules are properly codified and stored in a Cellular Nonlinear Network behaving as a memory. A numerical example...

In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and...

In this paper, we focus on the use of knowledge bases in two different application areas – control of systems with unknown or strongly nonlinear models (i.e. hardly controllable by the classical methods), and robot motion planning in eight directions. The first one deals with fuzzy logic and the paper presents approaches for setting and aggregating the rules of a knowledge base. Te second one i...