An analysis of four uncertainty calculi

Henkind, Steven J.; Harrison, Malcolm C.

doi:10.1109/21.21598

Cited by 89 publications

(44 citation statements)

References 40 publications

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“…The total evidence or belief, Pl(A) in Equation 27, represents, moreover, the additional evidence or belief corresponding to the focal elements overlapping with A (Henkind and Harrison, 1988;Klir and Parviz, 1992). Thus, the relation between the two dual measures is:…”

Section: Belief and Plausibility Measuresmentioning

confidence: 99%

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Huang

Liu

et al. 2013

American Journal of Engineering and Applied Sciences

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Engineering design under uncertainty has gained considerable attention in recent years. A great multitude of new design optimization methodologies and reliability analysis approaches are put forth with the aim of accommodating various uncertainties. Uncertainties in practical engineering applications are commonly classified into two categories, i.e., aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty arises because of unpredictable variation in the performance and processes of systems, it is irreducible even adding more data or knowledge. On the other hand, epistemic uncertainty stems from lack of knowledge of the system due to limited data, measurement limitations, or simplified approximations in modeling system behavior and it can be reduced by obtaining more data or knowledge. More specifically, aleatory uncertainty is naturally represented by a statistical distribution and its associated parameters can be characterized by sufficient data. If, however, the data is limited and can be quantified in a statistical sense, epistemic uncertainty can be considered as an alternative tool in such a situation. Of the several optional treatments for epistemic uncertainty, possibility theory and evidence theory have proved to be the most computationally efficient and stable for reliability analysis and engineering design optimization. This study first attempts to provide a better understanding of uncertainty in engineering design by giving a comprehensive overview of its classifications, theories and design considerations. Then a review is conducted of general topics such as the foundations and applications of possibility theory and evidence theory. This overview includes the most recent results from theoretical research, computational developments and performance improvement of possibility theory and evidence theory with an emphasis on revealing the capability and characteristics of quantifying uncertainty from different perspectives. Possibility and evidence theory-based reliability methods have many advantages for practical engineering when compared with traditional probability-based reliability methods. They can work well under limited data while the latter need large amounts of information, more than possible in engineering practice due to aleatory and epistemic uncertainties. The possible directions for future work are summarized.

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Section: Belief and Plausibility Measuresmentioning

confidence: 99%

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Huang

Liu

et al. 2013

American Journal of Engineering and Applied Sciences

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“…Abruptness is caused by increasing demand dominance over actual inventory level, which is shifted from low to medium to high dominance, respectively. [5,9,13] 0.125 2.333 [6,10,14] 0.222 2.397 [6.7, 10.7, 11.7] 0.307 4.955 [7,11,15] 0.347 9.528 [8,12,16] 0.5 11 [9,13,17] 0 Table 2. The output order quantity does not always respond to small changes in the uncertain input demand.…”

Section: Performance Analysismentioning

confidence: 99%

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Petrov́ić

Sweeney

1994

Computer Integrated Manufacturing Systems

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“…First, fuzzy logic can reduce the development time compared with other techniques. In Bayesian calculus for example, prior probabilities need to be acquired by means of a statistical analysis requiring massive amounts of data [86]. With fuzzy logic, it is possible to have a running system by using only an intuitive, common-sense description of the problem.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…Second, fuzzy logic is flexible; it can be built on top of the expert knowledge, mixed with conventional control methods and easy to add or change functionality. Third, FIS are computationally fast [86], which is important because the processing capabilities of sensor nodes are limited. Fourth, FIS can be implemented with little memory overhead, which is a desirable property in WSNs because of (1) the limited memory on the sensor nodes and (2) the latency of the network reprogramming.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…In particular, sensor inaccuracy or faults can adversely affect the overall robustness of the system below the acceptable threshold for industrial applications. Sensor data fusion techniques alleviate this problem, but also require complex computations that sensor nodes cannot handle, or prior information that is infeasible to obtain practically [86].…”

Section: Introductionmentioning

confidence: 99%

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Collaborative wireless sensor networks in industrial and business processes

Marin-Perianu¹

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An analysis of four uncertainty calculi

Cited by 89 publications

References 40 publications

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Possibility and Evidence-Based Reliability Analysis and Design Optimization

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Collaborative wireless sensor networks in industrial and business processes

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