loading
Maxitive Belief Structures and Imprecise Possibility Distributions
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

We introduce the idea of maxitive belief structures, MBS, as a framework for modeling imprecise possibility distributions in a manner analogous to the way Dempster-Shafer belief structures allows the modeling of imprecise probability distributions....

On Distributed Fuzzy Decision Trees for Big Data
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose...

A Novel Mechanism to Fuse Various Sub-Aspect Brain-Computer Interface (BCI) Systems with PSO for Motor Imagery Task
2015 Edition, October 1, 2015 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

In this study, we develop a novel multi-fusion brain-computer interface (BCI) system based on a fuzzy neural network (FNN) and information fusion approaches to cope with a classification task for identifying right/left hand motor...

Cooperative localisation using posterior linearisation belief propagation
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

This paper presents the posterior linearisation belief propagation (PLBP) algorithm for cooperative localisation in wireless sensor networks with nonlinear measurements. PLBP performs two steps iteratively: linearisation and belief propagation. At the...

Equipment health management through information fusion for reliability
2011 Edition, January 1, 2011 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

A generic framework for estimating the reliability of equipment is through "information fusion" of its failure history, predictive maintenance data and domain expert's knowledge is proposed and demonstrated. The framework uses "Degree of Certainty"...

Attribute Reduction Methods Based on Pythagorean Fuzzy Covering Information Systems
2020 Edition, Volume 8, January 1, 2020 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

By introducing covering rough sets to Pythagorean fuzzy environment, we construct a new rough set model called the Pythagorean fuzzy λ-covering rough set. Based on the rough set model, we adopt the discernibility matrix method to obtain its attribute reduction. First, we give...

Belief reliability for uncertain random systems
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Measuring system reliability by a reasonable metric is a basic problem in reliability engineering. Since the real systems are usually uncertain random systems which is affected by both aleatory and epistemic uncertainties, the existed reliability...

Calculating Dempster-Shafer plausibility
1991 Edition, Volume 13, June 1, 1991 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

A sufficient condition for the equality of the plausibility and commonality measures of the Dempster-Shafer belief calculus is developed. When the condition is met, an efficient method to calculate relative plausibility is available. In...

Fuzzy risk sets for decision making
2014 Edition, November 1, 2014 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The information available to the system is incomplete in many applications like Decision Support Systems, Control Systems and Medical Expert Systems. Sometimes decision has to be taken under risk with incomplete...

Fuzzy TOPSIS Algorithm for Multiple Criteria Decision Making with an Application in Information Systems Project Selection
2008 Edition, October 1, 2008 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Information systems project selection is a multi-criteria decision-making (MCDM) problem. This work develops a fuzzy approach based on the technique for order performance by similarity to ideal solution (TOPSIS), to choose optimal information...

Advertisement