Bayesian Network Parameter Learning Algorithm for Target Damage Assessment | Zendy

Xiuli Du | Zendy; Guichuan Fan | Zendy; Yana Lv | Zendy; Shaoming Qiu | Zendy

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Bayesian Network Parameter Learning Algorithm for Target Damage Assessment

Author(s) -

Xiuli Du,

Guichuan Fan,

Yana Lv,

Shaoming Qiu

Publication year - 2019

Publication title -

iop conference series materials science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1757-899X

pISSN - 1757-8981

DOI - 10.1088/1757-899x/569/5/052014

Subject(s) - bayesian network , gibbs sampling , expectation–maximization algorithm , algorithm , computer science , stability (learning theory) , wake sleep algorithm , maximization , bayesian probability , range (aeronautics) , population based incremental learning , set (abstract data type) , conditional probability , artificial intelligence , machine learning , mathematics , mathematical optimization , maximum likelihood , genetic algorithm , statistics , engineering , generalization error , programming language , aerospace engineering

Aiming at the problem that the existing methods of target damage assessment based on Bayesian network mainly determine the structural parameters of Bayesian network by giving conditional probability tables based on expert experience, which results in too subjective and having large errors in the evaluation results, an improved learning algorithm of conditional probability tables of Bayesian network is proposed in this paper. We divided the E step of the EM algorithm (Expectation Maximization Algorithm) into three steps. Firstly, the range of the missing variable is determined by expert experience, then the Gibbs sampling algorithm is used to complete the sample set, and finally the sample is weighted. The proposed algorithm is compared with EM algorithm, Gibbs algorithm, EM and Gibbs algorithm. The experimental results show that the proposed algorithm has good stability and high precision.

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