20,951 research outputs found
The belief noisy-or model applied to network reliability analysis
One difficulty faced in knowledge engineering for Bayesian Network (BN) is
the quan-tification step where the Conditional Probability Tables (CPTs) are
determined. The number of parameters included in CPTs increases exponentially
with the number of parent variables. The most common solution is the
application of the so-called canonical gates. The Noisy-OR (NOR) gate, which
takes advantage of the independence of causal interactions, provides a
logarithmic reduction of the number of parameters required to specify a CPT. In
this paper, an extension of NOR model based on the theory of belief functions,
named Belief Noisy-OR (BNOR), is proposed. BNOR is capable of dealing with both
aleatory and epistemic uncertainty of the network. Compared with NOR, more rich
information which is of great value for making decisions can be got when the
available knowledge is uncertain. Specially, when there is no epistemic
uncertainty, BNOR degrades into NOR. Additionally, different structures of BNOR
are presented in this paper in order to meet various needs of engineers. The
application of BNOR model on the reliability evaluation problem of networked
systems demonstrates its effectiveness
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The management of intelligence-assisted finite element analysis technology
Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA
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