In this paper, two types of fuzzy probability spaces will be introduced and their applications in methods of decision making under risk (especially in the Decision Matrix Method) will be described. First, a fuzzy probability space that generalizes the classical probability space (<,Bn, p) to the situation of fuzzy random events will be studied. It will be applied to perform fuzzy discretization...

2 Zadeh-type fuzzy probability The paper deals with two methods of a fuzzification of the Borel field of events and too the probability measure. The first approach generalizes the Zadeh definition of a crisp probability of fuzzy event. The second method is based on the Yager definition of a fuzzy probability of fuzzy event. The theoretical results obtained can be applied to modeling stochastic ...

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...

-In this work we present the methods of creating the knowledge bases by using the theory of fuzzy systems as well as the probability and stochastic processes theory. We show that such knowledge-based systems can be applied in different diagnostic tasks. The structure of the reason-result fuzzy model is predefined by experts at the beginning of the task. The notion of probability of fuzzy events...

In this paper, two types of fuzzy probability spaces will be introduced and their possible applications in methods of decision-making under risk will be described. First, a fuzzy probability space that generalizes the classical probability space ( ,Bn, p) to the situation of fuzzy random events will be studied. It can be applied e.g. when given continuous probability distributions of risk facto...

Rough set theory has attracted much attention in modeling with imprecise and incomplete information. A generalized approximation space, called fuzzy probability approximation space has been proposed by introducing probability into fuzzy approximation space. The novel definition combines three types of uncertainty into a model. Information or knowledge is considered as a partition of the univers...

To enhance the evaluation of the reliability recently featured many of the approaches linking the probability and fuzzy logic, the multi agent system can provide a connection between fuzzy logic and probability. In this article represents a new application of multi-agent model for evaluating the reliability indices of generation power system. Using agents of the probability for determining capa...

A categorical approach to probability allows to put basic notions of probability into a broader mathematical perspective, to evaluate their roles, and mutual relationships. Classical probability theory and fuzzy probability theory lead to two particular categories and their relationship (in categorical terms) enable us to understand and explicitly formulate the difference between them. Using ou...

This paper discusses the relationship between probability and fuzziness based on the process of perception. As a generalization of crisp set, fuzzy set is used to model fuzzy event as proposed by Zadeh. Similarly, we may consider rough set to represent rough event in terms of probability measure. Special attention will be given to conditional probability of fuzzy event as well as conditional pr...

Propagating possibilistic and probabilistic variables through a mapping yields a fuzzy random variable. We propose a method to attach probability intervals to events pertaining to the output variable. We show that this method is consistent with classical approaches to fuzzy random variables and that the obtained probability interval is the mean value of the fuzzy probability defined by viewing ...