anti-lock braking system (abs) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. for this reason an interval type-2 fuzzy controller is de...

Data in real world applications is often subject to some kind of uncertainty, which can be due to incompleteness, unreliability or inconsistency. This poses a great challenge for ontology-based data access (OBDA) applications, which are expected to provide a meaningful answers to queries, even under uncertain domains. Several extensions of classical OBDA systems has been proposed to address thi...

Multiattribute decision making involves tradeoffs among alternative performances over multiple attributes. The accuracy of performance measures are usually assumed to be accurate. Most multiattribute models also assume given values for the relative importance of weights for attributes. However, there is usually some uncertainty involved in both of these model inputs. Outranking multiattribute m...

Map-Based Localization approaches use a local map of the sensed environment that is matched against a previously stored map to correct the robot localization in the world. In many cases these methods are based on a probabilistic representation of the spatial uncertainty and use the Kalman Filter (KF) or the Extended Kalman Filter (EKF) to update the robot’s location estimation. On the other han...

This paper discusses two topics on fuzzy random variables in decision making. One is a new evaluation method of fuzzy random variables, and the other is to present a mathematical model in financial engineering by fuzzy random variables. The evaluation method is introduced as mean values defined by fuzzy measures, and it is also applicable to fuzzy numbers and fuzzy stochastic process defined by...

Fuzzy techniques have been originally designed to describe imprecise (“fuzzy”) expert knowledge. Somewhat surprisingly, fuzzy techniques have also been successfully used in situations without expert knowledge, when all we have is data. In this paper, we explain this surprising phenomenon by showing that the need for optimal processing of data (including crisp data) naturally leads to fuzzy and ...

Imprecise choices can be described using either a probabilistic or a fuzzy formalism. No relation between them has been studied so far. In this contribution we present a connection between the two formalisms that strongly makes use of fuzzy implication operators and t-norms. In this framework, Luce’s Choice Axiom turns out to be a special case when the product t-norm is considered and other sim...

Probabilistic and . . . Interval . . . Why Not Maximum . . . Chip Design: Case . . . General Approach: . . . Interval Approach: . . . Extension of Interval . . . Successes (cont-d) Challenges Problem Main Idea: Use Moments Formulation of the . . . Result Case Study: . . . General Problem Case Study: Detecting . . . Outlier Detection . . . Outlier Detection . . . Fuzzy Uncertainty: In . . . Ackn...

Probabilistic models can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty. In this paper, we present the development of a chain graph for assessing the risks associated with mental health problems, which is a domain that has high amounts of inherent uncertainty. The Galatean mental health Risk ...

Preferences and uncertainty occur in many real-life problems. The theory of possibility is one non-probabilistic way of dealing with uncertainty, which allows for easy integration with fuzzy preferences. In this paper we consider an existing technique to perform such an integration and, while following the same basic idea, we propose various alternative semantics which allow us to observe both ...