Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10346665 | Computers & Operations Research | 2005 | 19 Pages |
Abstract
This work proposes a novel approach for solving abductive reasoning problems in Bayesian networks involving fuzzy parameters and extra constraints. The proposed method formulates abduction problems using nonlinear programming. To maximize the sum of the fuzzy membership functions subjected to various constraints, such as boundary, dependency and disjunctive conditions, unknown node belief propagation is completed. The model developed here can be built on any exact propagation methods, including clustering, joint tree decomposition, etc.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Han-Lin Li, Han-Ying Kao,