Add to ORKGIn many engineering problems, to estimate the desired quantity, we process measurement results and expert estimates. Uncertainty in inputs leads to the uncertainty in the result of data processing. In this paper, we show how the existing feasible methods for propagating the corresponding interval and fuzzy uncertainty can be extended to new cases of potential practical importance
In many practical situations, we need to know how uncertainty propagates through data processing a...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
AbstractIt is known that processing of data under general type-1 fuzzy uncertainty can be reduced to...
In many practical application, we process measurement results and expert estimates. Measurements and...
In many practical application, we process measurement results and expert estimates. Measurements and...
In many practical application, we process measurement results and expert estimates. Measur...
In many practical application, we process measurement results and expert estimates. Measurements and...
In many practical situations, we are interested in statistics characterizing a population of objects...
Data uncertainty affects the results of data processing. So, it is necessary to find out how the dat...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...
Traditional statistical data processing techniques (such as Least Squares) assume that we know the p...
In many industrial engineering problems, we must select a design, select parameters of a process, or...
Since the 1960s, many algorithms have been designed to deal with interval uncertainty. In the last d...
In many engineering applications, we have to combine probabilistic, interval, and fuzzy uncertainty....
In dealing with representing knowledge under uncertainty there is a sustained tendency to increase f...
In many practical situations, we need to know how uncertainty propagates through data processing a...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
AbstractIt is known that processing of data under general type-1 fuzzy uncertainty can be reduced to...
In many practical application, we process measurement results and expert estimates. Measurements and...
In many practical application, we process measurement results and expert estimates. Measurements and...
In many practical application, we process measurement results and expert estimates. Measur...
In many practical application, we process measurement results and expert estimates. Measurements and...
In many practical situations, we are interested in statistics characterizing a population of objects...
Data uncertainty affects the results of data processing. So, it is necessary to find out how the dat...
On various examples ranging from geosciences to environmental sciences, this book explains how to ge...
Traditional statistical data processing techniques (such as Least Squares) assume that we know the p...
In many industrial engineering problems, we must select a design, select parameters of a process, or...
Since the 1960s, many algorithms have been designed to deal with interval uncertainty. In the last d...
In many engineering applications, we have to combine probabilistic, interval, and fuzzy uncertainty....
In dealing with representing knowledge under uncertainty there is a sustained tendency to increase f...
In many practical situations, we need to know how uncertainty propagates through data processing a...
Uncertainty is ubiquitous. Depending on what information we have, we get different types of uncertai...
AbstractIt is known that processing of data under general type-1 fuzzy uncertainty can be reduced to...