Fuzzy intervals as a basis for measurement theory

Int. J. Unc. Fuzz. Knowl. Based Syst.

Zuo

1997

Usually, expert systems use numbers to describe the experts' degree of belief in their statements. In practice, however, it is difficult to assign an exact numerical value to the expert's degree of belief. At best, we can get an interval of possible values. This fact leads to the use of interval-valued degree of belief. When intervals are used to describe degrees of belief, then computations with intervals must be used to process them. In this paper, we describe applications of such interval computations to expert systems and to intelligent control.

Section: Applications To Expert Systemsmentioning

confidence: 99%

Interval-Valued Degrees of Belief: Applications of Interval Computations to Expert Systems and Intelligent Control

Nguyen

Int. J. Unc. Fuzz. Knowl. Based Syst.

Zuo

1997

“…As the membership function of measurand we offer to use symmetric curvilinear trapezium, which has height equal to one and presents information about characteristics of systematic error and total error [2]:…”

Section: Introductionmentioning

confidence: 99%

Measurement's Result and its Error as Fuzzy Variables: Background and Perspectives

Solopchenko

Semenov

et al. 2010

KEM

“…In a number of the international [11,13,14] and national standards, the term "measurement error" has been replaced with the term "measurement uncertainty", which can be considered as more correspondent to fuzzy systems terminology. Publications, criticizing the probabilistic models applied in measurement science, are now followed by a number of works, trying to formulate those models from the fuzzy theory point of view or to combine both theories [15][16][17][18][19][20][21][22]. For example, in [6,7] a priori fuzzy information about the object under measurement is applied to increase the measurement accuracy and/or reliability.…”

Section: Representation Of Uncertain Information and Uncertainty Ementioning

confidence: 99%

Fuzzy and probabilistic models of association information in sensor networks

Reznik

2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542)

-The paper considers the problem of improving accuracy and reliability of measurement information acquired by sensor networks. It offers the way of integrating sensor measurement results with association information available or a priori derived at aggregating nodes. The models applied for describing both sensor results and association information are reviewed with consideration given to both neuro-fuzzy and probabilistic models and methods. The information sources, typically available in sensor systems, are classified according to the model (fuzzy or probabilistic), which seems more feasible to be applied. The integration problem is formulized as an optimization problem.