In this paper, we present an enhancement of the fuzzy connectedness-based image segmentation method based on dynamic computation of adaptive weights for the homogeneity and the directional gradient energy functions. Adaptive weights enhance the performance and robustness of the conventional fuzzy connectedness-based segmentation while decreasing the degree of user interaction. The accuracy of t...

This paper describes a genetic-fuzzy approach for controlling a nonlinear model of the HIV immunology. The approach is set up to find mamdani fuzzy controllers capable of boosting the immune response while reducing the systemic cost to the body due to the use of high efficacy drugs. General aspects of the genetic fuzzy system are described while special emphasis is given to control results. The...

Paraconsistent logics are specially tailored to deal with inconsistency, while fuzzy logics primarily deal with graded truth and vagueness. In the last decade, mathematical fuzzy logic has been developed as a discipline studying formal many-valued systems arising from fuzzy set theory. In this paper we study to what extent different systems of fuzzy logic are paraconsistent, identifying which f...

The paper presents the comparison of fuzzy conclusions derived from the use of t-norms with the fuzzy conclusions derived from the use of H-norm. The idea was to examine the application of H-logical norm to fuzzy reasoning, which is not monotonious while it is known that t-norms used in fuzzy reasoning have the characteristic of monotonicity. The comparison of fuzzy conclusions was performed by...

Fuzzy sets are the most signi…cant tools to handle uncertain data while neutrosophic sets are the generalizations of fuzzy sets in the sense to handle uncertain, incomplete, inconsistent, indeterminate, false data. In this paper, we introduced fuzzy subspaces and neutrosophic subspaces (generalization of fuzzy subspaces) by applying group actions.Further, we de…ne fuzzy transitivity and neutros...

A fuzzy logic toolkit has been developed for the formal specification language Z. It permits the incorporation of fuzzy concepts into the language while retaining the precision of any Z specification. The toolkit provides the necessary operators, measures and modifiers for the definition and manipulation of fuzzy sets and relations. This paper illustrates how the toolkit can be used to specify ...

a real-time optimization (rto) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. furthermore, the objective function is penalized by a fuzzified form of the key process constraints. to enable using conventional optimization techniques, the resulting fuzzy optimization problem is then re...

Fuzzy systems address the imprecision of the input and output variables, which formally describe notions like “rather warm” or “pretty cold”, while provide a behaviour that depends on fuzzy data. This class of systems are classically represented by means of Fuzzy Inference Systems (FIS), a computing framework based on the concepts of fuzzy if-then rules and fuzzy reasoning. Even if FIS are larg...

Fuzzy inference systems and neural networks are complementary technologies in the design of adaptive intelligent systems. Artificial Neural Network (ANN) learns from scratch by adjusting the interconnections between layers. Fuzzy Inference System (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. A neuro-fuzzy system is sim...