基于粗糙集与贝叶斯网络的推理和诊断模型

A reasoning and diagnosis model based on rough sets and bayesian networks

基于粗糙集知识约简、规则提取的优势和贝叶斯网络强大的推理能力,对决策表进行约简和提取决策规则获取节点关系、节点概率分布等信息,以获取的信息为基础建立贝叶斯网络模型。然后通过贝叶斯网络实现进行高效快速的推理和诊断。实例分析表明,该方法是可行有效的。

Based on rough set knowledge reduction, rules extraction and the powerful bayesian Network reasoning ability, the reduction of the decision-making table and the extraction of decision rules help obtain such information as the relationship between nodes and the node probability distribution, thus establishing a bayesian network model. Next the bayesian network can be applied to realize fast and efficient reasoning and diagnosis. And the analysis of examples shows that this method is feasible and effective.