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中文摘要：
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。
英文摘要：
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. It is capabe of inferring walk modulus k and window modulus b in term of unsmooth grade modulus rb and adjusting modulus m, data time Δt through fuzzy logical and the variable structure DBN can be gained through the designed frame. In this paper, the general variable structure DBN learning model frame and the whole algorithm are presented. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
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