基于贝叶斯概率语义网的铝电解槽况知识表示模型与约简方法

铝电解生产过程中的知识具有跨领域、不确定、多源异构等特征,采用传统知识表示方法将导致组合爆炸、多义性、知识选择困难及可理解性差,从而降低铝电解槽况判断的效率及准确性.将贝叶斯条件概率与传统语义网相结合,提出一种新的可用于铝电解槽况判断的知识表示方法.该方法分别采用知识元和概率做关联和乘法运算,可有效解决知识在推理过程中出现多义性和知识选择困难的问题;同时,提出基于组合消除的知识约简方法,旨在解决关联关系矩阵中重复知识因子多、矩阵维数高导致存储和计算困难的问题.最后通过铝电解槽况判断的案例分析,验证了贝叶斯概率语义网模型的合理性、可行性和有效性.

Aluminum electrolysis cell condition knowledge representation model and reduction method based on Bayesian probability semantic network

The knowledge about aluminum electrolysis production has properties of interdisciplinarity, uncertainty, multi-source and heterogeneity. Using the traditional knowledge representation may give rise to the problems like combination explosion, ambiguity, knowledge selection difficulty, poor understandability and so on, all of which will thereby reduce the efficiency and accuracy of the condition diagnosis of aluminum electrolytic cells. To solve the above problems, a new knowledge representation method that combines Bayesian conditional probability and traditional semantic net is proposed in the paper, and it is named the knowledge representation model based on Bayesian probability semantic network. In this method, the knowledge element and the probability are used for correlation and multiplication respectively, which can solve effectively the problems of ambiguity, knowledge selection difficulty and poor understandability in the process of knowledge reasoning. Meanwhile, knowledge reduction method based on combination elimination is proposed, aiming to solve the storage and calculation problems caused by redundantly repeated knowledge factors and high matrix dimension in the relational matrix. The rationality, feasibility and effectiveness of the Bayesian probability semantic network model are verified by a case study of the aluminum electrolytic cells condition diagnosis.