Discrete dynamic BN parameter learning under small sample and incomplete information

Jia Ren, Xiao Guang Gao, Yong Bai

科研成果: 期刊稿件文章同行评审

9 引用 (Scopus)

摘要

Aiming at the discrete dynamic Bayesian network parameter learning under the situation of small sample and incomplete information, a constraint recursion learning algorithm is presented. The forward algorithm is used to establish a parameter recursion estimation model of discrete dynamic Bayesian network with hidden variables. A prior parameter constraint model with uniform distribution is established with the present network parameters as variables. Then the approximate Beta distribution could be acquired through the optimization algorithm. Finally, the distribution of prior parameter knowledge could be used in the above model of recursive estimation to finish the parameter learning process. The method is applied to the unmanned aerial vehicle dynamic model of threat assessment. The results show the effectiveness and accuracy of the proposed algorithm.

源语言英语
页(从-至)1723-1728
页数6
期刊Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
34
8
DOI
出版状态已出版 - 8月 2012

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