A (propositional) logic L is paraconsistent with respect to a negation connective if P; P 6`L Q in case P and Q are two distinct atomic variables. Intuitively (and practically) the logic(s) we use should be paraconsistent (with respect to any unary connective!) on the ground of relevance: why should a \contradiction" concerning P imply something completely unrelated? Nevertheless, the most know...

Each fuzzy set can be represented by a nested system of ordinary sets—its a-cuts. There is an extensive literature on fuzzy sets devoted to problems of the following kind: is it possible to reduce operations with fuzzy sets to operations with their a-cuts? Is it possible to reduce properties of fuzzy relations to properties of their a-cuts? More generally, can a fuzzy concept be represented by ...

We present a procedure for the construction of multi-valued t-norms and t-conorms. Our procedure makes use of a pair of single-valued t-norms and the respective dual t-conorms and produces interval-valued t-norms u and t-conorms t. In this manner we combine desirable characteristics of different t-norms and t-conorms; if we use the t-norm ∧ and t-conorm ∨, then (X,u,t) is a superlattice, i.e. t...

We present a family of algebraic structures, called rectangular bilattices, which serve as a natural accommodation and powerful generalization to both intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IVFSs). These structures are useful on one hand to clarify the exact nature of the relationship between the above two common extensions of fuzzy sets, and on the other hand provide ...

Among various approaches to fuzzy logics, we have chosen two of them, which are built up in a similar way. Although starting from diierent basic logical connectives, they both use interpretations based on Frank t-norms. Diierent interpretations of the implication lead to diierent axiomatizations, but most logics studied here are complete. We compare the properties, advantages and disadvantages ...

The main goal of this paper is to apply the method of extension of fuzzy connectives proposed in our previous work for fuzzy implications valued on a bounded lattice. Also we discuss about which properties of implications are preserved by this method and we prove some results involving extension and automorphisms. Finally, we investigate the behavior of the extensions of two special classes of ...

Active Learning Method (ALM) is a soft computing method which is used for modeling and control, based on fuzzy logic. Although ALM has shown that it acts well in dynamic environments, its operators cannot support it very well in complex situations due to losing data. Thus ALM can find better membership functions if more appropriate operators be chosen for it. This paper substituted two new oper...

In this paper we shows the experimental results using a microcontroller and hardware integration with the EMC2 software, using the Fuzzy Gain Scheduling PI Controller in a mechatronic prototype. The structure of the fuzzy 157 Research in Computing Science 116 (2016) pp. 157–169; rec. 2016-03-23; acc. 2016-05-11 controller is composed by two-inputs and two-outputs, is a TITO system. The error co...

Fuzzy sets and rough sets are known as uncertainty models. They are proposed to treat different aspects of uncertainty. Therefore, it is natural to combine them to build more powerful mathematical tools for treating problems under uncertainty. In this chapter, we describe the state of the art in the combinations of fuzzy and rough sets dividing into three parts. In the first part, we first desc...

AbsikactFuzzy connectives use to be assumed associative. In this way, key operational difficulties are solved by means of a single binary operator. In this paper we point out that the main property in order to assure operativeness should be recurszveness, which is weaker than associativity. If calculus can be obtained by means of a recursive application of a sequence of binary connectives, we s...