Reasoning computation based on causality diagram under uncertainty and continuous variables

Xinyuan Liang; Qingxi Shi; Qin Zhang

doi:10.1117/12.664289

20 February 2006 Reasoning computation based on causality diagram under uncertainty and continuous variables

Xinyuan Liang, Qingxi Shi, Qin Zhang

Proceedings Volume 6041, ICMIT 2005: Information Systems and Signal Processing; 60410C (2006) https://doi.org/10.1117/12.664289
Event: ICMIT 2005: Merchatronics, MEMS, and Smart Materials, 2005, Chongqing, China

Abstract

Reasoning computation under uncertainty is an important issue in intelligent systems. A dynamic causality trees/diagram was developed to deal with uncertainty of complex systems. It has important theoretical meaning and application value for fault diagnosis. However, just like most existing methods, it considers only discrete cases and thus restricts its applications. In this paper, a new method is proposed to deal with continuous cases in which the ascendant, descendent and linkage variables can be continuous while keeping them independent of each other. The uncertainty reasoning computation under continuous variables was disposed by calculation for possibility distribution and computation of conditional probability density function. This intelligent computation method gives a series of probability density function, which helps to compute probability of events for fault diagnosis. Simulation result shows that the computation is effective for fault diagnosis.

Citation Download Citation

Xinyuan Liang, Qingxi Shi, and Qin Zhang "Reasoning computation based on causality diagram under uncertainty and continuous variables", Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 60410C (20 February 2006); https://doi.org/10.1117/12.664289

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