Semantic Network Based on Intuitionistic Fuzzy Directed Hyper-Graphs and Application to Aluminum Electrolysis Cell Condition Identification

In complex industrial processes, the knowledge has properties of multi-source heterogeneity, polymorphism, and uncertainty. When the conventional knowledge representation methods are used to represent this type of knowledge, they often result in misunderstanding, inexplicability, and ambiguity. To solve this problem, a semantic network based on intuitionistic fuzzy directed hyper-graphs (SN-IFDHGs) model is proposed. First, qualitative knowledge is transformed to quantitative knowledge using an intuitionistic fuzzy algorithm. In the SN-IFDHG model, an edge set can connect multiple vertexes, which mean multi-source knowledge elements. Meanwhile, to present uncertain knowledge, the weights between semantic nodes are characterized by simultaneously containing both membership and non-membership. Then, to reduce the space complexity and facilitate the reconstruction of the SN-IFDHG model, a novel storage structure based on in-degree index list is proposed. Finally, a knowledge reasoning method based on entropy weight of SN-IFDHG is proposed and applied to aluminum electrolysis cell condition identification. The experimental results show that the proposed knowledge reasoning method is more effective and accurate than other existing algorithms.

Abstract licence: CC BY-NC-ND

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