非平稳随机系统动态贝叶斯结构学习关系网

 针对基于图模型自主控制中环境感知问题，提出了连续变量非平稳系统变结构动态贝叶斯网络(DBN)的结构学习模型;讨论了平稳系统常结构DBN学习模型。以上述结论为基础，提出了模糊自适应尺度法用于非平稳系统变结构DBN结构学习，依据非平稳程度函数rb、调节系数m及区间长度Δt模糊推理出步长系数k和窗口系数b，依据具体框架得到变结构DBN，给出了连续变量非平稳系统变结构DBN学习的模型框架和具体算法。仿真结果表明：提出的结构学习框架是可行的。

Relation Networks of Dynamic Bayesian Structure Learning in Non-stationary Random System

The continuous variable non-stationary systems learning model is problem for dynamic Bayesian networks(DBN) with variable structure for the problem of graph model based environment perception in autonomous control. Firstly, constant DBN structure learning model in smooth random system is discussed, and the Bayesian information criterion(BIC) score to continuous hide variable DBN and structure learning frame are researched. Secondly, on the basis of constant DBN, the fuzzy self-adapt measure algorithm is presented to learn the variable DBN structure in unsmooth random  system.