Coupled Probabilistic And Possibilistic Uncertainty Estimation In Rule-Based Analysis Systems

L. Tsoukalas; M. Ragheb

doi:10.1117/12.940605

11 May 1987 Coupled Probabilistic And Possibilistic Uncertainty Estimation In Rule-Based Analysis Systems

L. Tsoukalas, M. Ragheb

Proceedings Volume 0786, Applications of Artificial Intelligence V; (1987) https://doi.org/10.1117/12.940605
Event: 1987 Technical Symposium Southeast on Optics, Electro-Optics, and Sensors, 1987, Orlando, FL, United States

Abstract

A methodology is developed for estimating the Performance of monitored engineering devices. Inferencing and decision-making under uncertainty is considered in Production-Rule Analysis systems where the knowledge about the system is both probabilistic and possibilistic. In this case uncertainty is considered as consisting of two components: Randomness describing the uncertainty of occurrence of an object, and Fuzziness describing the imprecision of the meaning of the object. The concepts of information granularity and of the probability of a fuzzy event are used. Propagation of the coupled Probabilistic and possibilistic uncertainty is carried out over model-based systems using the Rule-Based paradigm. The approach provides a measure of both the performance level and the reliability of a device.

Citation Download Citation

L. Tsoukalas and M. Ragheb "Coupled Probabilistic And Possibilistic Uncertainty Estimation In Rule-Based Analysis Systems", Proc. SPIE 0786, Applications of Artificial Intelligence V, (11 May 1987); https://doi.org/10.1117/12.940605

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