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

Zuguo Chen | Zendy; Yonggang Li | Zendy; Xiaofang Chen | Zendy; Chunhua Yang | Zendy; Weihua Gui | Zendy

Open Access

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

Author(s) -

Zuguo Chen,

Yonggang Li,

Xiaofang Chen,

Chunhua Yang,

Weihua Gui

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2752200

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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