Fuzzy relations are simple mathematical structures that enable a very general representation of fuzzy knowledge, and fuzzy relational calculus offers a powerful machinery for approximate reasoning. However, one of the most relevant limitations of approximate reasoning is the efficiency bottleneck. In this paper, we present a module for fast fuzzy inference through relational composition, with t...

This paper proposes a fuzzy approach for reasoning about action and change in timed domains. In our method, actions and world states are modeled as fuzzy sets over time axis. Thus, their temporal relations and time constraints can be model as fuzzy rules. So, our method handles well the issue that action happens at an approximate time and then the states change also at an approximate time, whic...

with conventional expert systems, it is clear that 1) 2) 3) AARS can reduce the size of the rule base; AARS uses linguistic variables rather than quantitative variables; AARS increases the system's flexibility. In future research, we expect to 1) associate observation P,' and rule R , with certainty factors CF, and CF, and derive a certainty factor for consequent experiment with other forms of ...

Since the appearance of the first paper on fuzzy sets proposed by Zadeh in 1965, the relationship between probability and fuzziness in the representation of uncertainty has been discussed among many people. The question is whether probability theory itself is sufficient to deal with uncertainty. In this paper the relationship between probability and fuzziness is analyzed by the process of perce...

This paper presents an approach to similarity based approximate reasoning that elucidates the connection between similarity and existing approaches to inference in approximate reasoning methodology. A set of axioms is proposed to compute a reasonable measure of similarity between two fuzzy sets. Such a measure of similarity is then used at different stages to derive a conclusion in approximate ...

this paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of articial neural networks, can get a real input vector and calculates its corresponding fu...

In this contribution, an implicative variant of the conjunctive normal form will be recalled and further studied. This normal form is an alternative to the Perfilieva’s conjunctive normal form. It will be shown that it is a suitable model for a particular case of graded fuzzy rules introduced as a generalization of classical fuzzy rules. Moreover, approximation properties of the implicative var...

‎In this paper we propose a method for computing approximations of solution of fuzzy fractional differential equations using fuzzy variational iteration method. Defining a fuzzy fractional derivative, we verify the utility of the method through two illustrative ‎examples.‎

Fuzzy relation equations are used to investigate theoretical and applicational aspects of fuzzy set theory, e.g., approximate reasoning, time series forecast, decision making and fuzzy control, etc.. This paper relates these equations to a particular kind of concept lattices.