Bayesian network structure learning method and system and reliability model construction method

Abstract

The invention provides a Bayesian network structure learning method and system and reliability model construction method. The learning method comprises the steps that relevant data of each module of a system are acquired and are abstracted into corresponding Bayesian network nodes; according to the relevant data of the system, a node mutual information matrix and a node to BIC scoring matrix are calculated and established; a corresponding adjacency matrix is established; and according to relevant data of the system, a family description length value corresponding to each node and the sum of the family description length values of all nodes are calculated. Bordered operation is carried out. Whether a BN structure continues to be updated is judged through a minimum description length principle. Side flipping is carried out. Whether the BN structure continues to be updated is judged through the minimum description length principle. In bordered operation, an MDL standard and a BIC evaluation standard are combined to avoid repeated climbing. The learning method still belongs to a greedy search algorithm. Due to combined specific scoring standards, the search efficiency and the accuracy rate are greatly improved.

Description

technical field [0001] The invention relates to the technical field of reliability engineering, in particular to a Bayesian network structure learning method, a learning system and a reliability model building method. Background technique [0002] Bayesian network (Bayesian network, BN) is a kind of directed acyclic graph, which is used to express the relationship and the degree of mutual influence among the variables of the system. Among them, the association relationship is expressed by directed arcs in the network; the influence degree between variables is described by the conditional probability table. System reliability can be understood as the probability that a particular system will work properly under given conditions. [0003] The system reliability estimation technology based on Bayesian network has been widely used in many fields. The effect of BN structure learning affects the accuracy of system reliability estimation to a great extent. Therefore, the key to ...

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Problems solved by technology

[0007] In view of the deficiencies of the above-mentioned prior art, the object of the present invention is to provide a Bayesian network structure learning method, a learning system and a reliability model building method, aiming at solving the problem of search efficiency and problems in the BN structure search and optimization process in the prior art. The problem of low accuracy

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