Optimization Models for Training Belief-Rule-Based Systems

J Liu; J Wang; D-L Xu; J-B Yang

doi:10.1109/TSMCA.2007.897606

Optimization Models for Training Belief-Rule-Based Systems

J Liu, J Wang, D-L Xu, J-B Yang

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	569-585
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Volume	37
Issue number	4
DOIs	https://doi.org/10.1109/TSMCA.2007.897606
Publication status	Published (in print/issue) - 1 Jul 2007

Bibliographical note

Other Details
------------------------------------
It is difficult to accurately determine rule-bases entirely subjectively, particularly for a large-scale rule-base. This paper presents optimisation models for a newly developed belief rule-base system. The models provide a non-black-box simulator for which input/output information can be incomplete or vague, either numerical or judgmental, or mixed. Application to analyse safety of engineering systems and leak detection in oil pipelines has shown their abilities to simulate real situations in a meaningful, efficient, consistent, and optimal way. The work relates to developments in two projects funded by EPSRC (GR/S85498/01) and DEFRA (AFM222) respectively, held in the University of Manchester.

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10.1109/TSMCA.2007.897606

Cite this

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title = "Optimization Models for Training Belief-Rule-Based Systems",

author = "J Liu and J Wang and D-L Xu and J-B Yang",

note = "Other Details ------------------------------------ It is difficult to accurately determine rule-bases entirely subjectively, particularly for a large-scale rule-base. This paper presents optimisation models for a newly developed belief rule-base system. The models provide a non-black-box simulator for which input/output information can be incomplete or vague, either numerical or judgmental, or mixed. Application to analyse safety of engineering systems and leak detection in oil pipelines has shown their abilities to simulate real situations in a meaningful, efficient, consistent, and optimal way. The work relates to developments in two projects funded by EPSRC (GR/S85498/01) and DEFRA (AFM222) respectively, held in the University of Manchester.",

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