基于Netica的自学习贝叶斯网络的构建

Construction of self-learning Bayesian network based on Netica

针对贝叶斯网络构建时参数与结构难以自适应调整,提出基于Netica的自学习贝叶斯网络的构建方法。首先根据Netica要求处理样本数据集,然后运用Netica基础函数开发结构学习模块和参数学习模块,进而能够构建出自动学习样本数据集的贝叶斯网络。同时,开发了概率推理模块和证据敏感性分析模块以评估所建网络的有效性。以国家电网的短路故障样本数据为例建立其自学习贝叶斯网络,实验构建的自学习贝叶斯网络能够实现不确定性推理,表明所提方法是贝叶斯网络功能实现的一个新途径。

Aiming at the limitation of adaptive adjustment of parameter and structure in process of constructing Bayesian network,a construction way of self-learning Bayesian network based on Netica software is presented.Firstly,the sample data set is processed according to the request of Netica. Then,structure learning module and parameter learning module is developed by using Netica basis function,in turn. Bayesian network is established by learning sample data set automatically. At the same time,probability references module and evidence sensitivity analysis module are developed to assess the effectiveness of the built network. The self-learning Bayesian network is established by using short-circuit fault sample data set from state grid,and the self-learning Bayesian network can implement the uncertainty reference. The result shows that the self-learning Bayesian network is feasible and effective,and it provides a new way for the realization of the Bayesian network.