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Proceedings Paper

Reasoning computation based on causality diagram under uncertainty and continuous variables
Author(s): Xinyuan Liang; Qingxi Shi; Qin Zhang
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Paper 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.

Paper Details

Date Published: 20 February 2006
PDF: 6 pages
Proc. SPIE 6041, ICMIT 2005: Information Systems and Signal Processing, 60410C (20 February 2006); doi: 10.1117/12.664289
Show Author Affiliations
Xinyuan Liang, Chongqing Univ. (China)
Chongqing Technology and Business Univ. (China)
Qingxi Shi, Chongqing Univ. (China)
Chongqing Technology and Business Univ. (China)
Qin Zhang, Chongqing Univ. (China)

Published in SPIE Proceedings Vol. 6041:
ICMIT 2005: Information Systems and Signal Processing
Yunlong Wei; Kil To Chong; Takayuki Takahashi, Editor(s)

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