Clustering is an unsupervised classification technique for grouping set of abstract objects into classes of similar objects. Clustering uncertain data is one of the essential tasks in mining uncertain data. Uncertain data is typically found in the area of sensor networks, weather data, customer rating data etc. The earlier methods for clustering uncertain data based on probability distribution,...

This paper proposes a dynamic output feedback controllers for variable structure systems. It is based on the concept of output feedback controllers, introduced by Z ak and Hui (IEEE Trans. Automat. Control AC-38 (1993) 1509) and Kwan (IEEE Trans. Automat. Control AC-41 (1996) 1691) for matched uncertain variable structure systems in which the state is unavailable and no estimated state is requi...

Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from measurement errors, data staleness, and repeated measurements etc., these kinds of uncertainty have to be handled cautiously, or else the mining results could be unreliable or even wrong. In this paper, we focus on class...

In addition to the four axioms of uncertainty theory, this paper presents the fifth axiom called product measure axiom. This paper also gives an operational law of independent uncertain variables and a concept of entropy of continuous uncertain variables. Based on the uncertainty theory, a new uncertain calculus is proposed and applied to uncertain differential equation, finance, control, filte...

This paper discusses the uncertain portfolio selection problem when security returns cannot be well reflected by historical data. It is proposed that uncertain variable should be used to reflect the experts’ subjective estimation of security returns. Regarding the security returns as uncertain variables, the paper introduces a risk curve and develops a mean-risk model. In addition, the crisp fo...

This paper investigates the single period inventory Newsboy problem in a general uncertain environment. Here it is assumed that the uncertainty appears in the market demand of the product. Uncertain demand is used to describe a subjective estimate (not estimated by random/fuzzy demand), linguistically expressed by ‘‘demand is about n’’. Using identification functions of the uncertain variable w...

This paper presents a new approach to the design of a composite sliding mode control for a class of chaotic systems with uncertainties. A significant feature of this control scheme is the incorporation of a new complementary sliding variable to the conventional sliding variable in order that a high-performance controller can be obtained. It has been shown that the guaranteed steady-state error ...

By confining the parametera in confident threshold 1 2 1 a £ < in rough set model based on uncertain measure, variable precision rough set model based on uncertain measure is given. The lower approximation and upper approximation are defined in approximation uncertainty space, and their properties are discussed. © 2011 World Academic Press, UK. All rights reserved.

Uncertainty theory is a branch of mathematics for modeling human uncertainty. This paper will answer the following questions: What is uncertainty? In what situations does uncertainty arise? What is the difference between uncertain variable and uncertain set? This paper will also discuss the relations and differences among uncertainty, fuzziness and probability. c ⃝2012 World Academic Press, UK....

where the future cost f depends both on the decision u ∈ U as well as the outcome of uncertain events, represented by a random variable X ∈ X . Here the random variable X follows a distribution P which is assumed to be known in order to form the expectation in problem (1). Examples includes making an inventory decision with uncertainty future demand, purchasing stocks with uncertain information...