图模型与学习算法结合的贝叶斯网络自动建模

针对纯数据驱动的贝叶斯网络结构学习算法的准确度和效率较低的问题，提出了一种融合多信号流图模型与K2学习算法的贝叶斯网络自动建模方法。该方法利用多信号流图模型能够描述信号之间传递与依赖关系的能力，结合K2学习算法在结构学习中的优势，实现了专家知识与数据驱动方法有效融合的贝叶斯网络结构自动学习算法。通过与常用网络结构学习算法的对比实验证明，该融合算法显著降低了结构学习对学习范围和训练数据规模的要求，具有更高的学习准确度和运算效率。采用真实系统实例阐述了该融合算法的应用过程，验证了算法的实用性。 

Automatic learning of Bayesian network structure using graph model and learning algorithm

In order to improve the accuracy and efficiency of the data-driven approaches in learning Bayesian network structure, expert knowledge is usually implemented in the learning algorithm. To deal with the lack of effective ways to combine the expert knowledge and the data-driven learning approaches in the existing methods, this paper proposes an automatic learning method for Bayesian network structure learning, which combines multi-signal flow graphs and learning algorithm K2. The method inserts expert knowledge into data-driven learning methods, using the information of relationships between signals from multi-signal flow graphs and the structure learning algorithm K2, to achieve automatic learning of Bayesian network structure. Numerical analysis, compared with other typical network structure learning algorithms, proves that the proposed method significantly lowers the structure learning requirements for learning scale and training data size and provides a higher learning accuracy and computation efficiency. The application of the proposed method is illustrated using a real engineering system and verified the practicability of the algorithm at the same time.