Imprecision, incompleteness and dynamic exist in wide range of network applications. It is difficult to decide the uncertainty relationship among nodes since traditional models do not make sense on uncertain networks, and the inherent computational complexity of problems with uncertainty is always intractable. In this paper, we study how to capture the uncertainty in networks by modeling a seri...

A major issue in clustering uncertain objects is related to the poor efficiency of existing algorithms, which is mainly due to expensive computation of the distance between uncertain objects. This paper discusses how we addressed this issue through an original formulation of the problem of clustering uncertain objects based on the minimization of the variance of the mixture models that represen...

Unlike the classical deterministic digital circuit analysis, we consider the analysis of digital circuits with uncertain inputs. Thus, given a binary function of n uncertain input binary variables, we introduce a new way of using K-maps to express the probability of this binary function in terms of the probabilities of the corresponding input binary variables. This in turn, allows us to estimat...

In this work, we address the problem of similarity search in a database of uncertain spatio-temporal objects. Each object is defined by a set of observations ((time,location)-tuples) and a Markov chain which describes the objects uncertain motion in space and time. To model similarity which is an important building block for many applications such as identifying frequent motion patterns or traj...

This paper presents the stability and robustness analyses of uncertain nonlinear control systems. TO proceed with the analysis, the uncertain nonlinear system is represented by a fuzzy model with uncertainties. Based on this model, under a single-grid-point (SGP) approach, a fuzzy controller is designed to close the feedback loop. A simple stability criterion is derived by estimating the norm o...

Uncertainty theory is a branch of axiomatic mathematics based on normality, monotonicity, self-duality, countable subadditivity, and product measure axioms. And uncertain variable is a fundamental concept in uncertainty theory which is used to represent imprecise quantities. This paper proposes a definition of quadratic entropy to characterize the uncertainty of uncertain variables resulting fr...

Redundancy optimization problem with cost constraints is a typical problem in reliability engineering. This study investigates standby redundancy optimization problem with uncertain lifetimes and uncertain costs. Three models for optimizing different performances of a standby system are formulated. In these models, maximizing the reliability measure, maximizing the system lifetime and minimizin...

This paper presents a new correlation coefficient measure, which satisfies the requirement of this measure equaling one if and only if two interval neutrosophic sets (INSs) are the same. And an objective weight of INSs is presented to unearth and utilize deeper information that is uncertain. Using the proposed weighted correlation coefficient measure of INSs, a decision-making method is develop...

Non-parametric DEA is a technique on the basic of mathematical programming to determine the efficiency of homological decision making units. DEA models changes in demand cause changes in variations in output levels and also will cause changes in a firm’s inefficiency. Often a firm can adjust input influencing on the output level. Models designed with technique on DEA that considers changes in d...

The fuzzy measure can highlight important information in analyzing component features, patterns, and trends. However, fuzzy densities and interaction effects are usually unknown or uncertain for implications thus making the fuzzy measure limited in applications. This research proposes an extended fuzzy measure to derive the conditional fuzzy densities from dominance-based rough set approach (DR...