Zhizhen Wang , Liu Fu , Meng Ma , Zhi Zhai , Hui Chen
{"title":"Extended ARMA graph neural networks for the prognosis of complex systems","authors":"Zhizhen Wang , Liu Fu , Meng Ma , Zhi Zhai , Hui Chen","doi":"10.1016/j.knosys.2024.112762","DOIUrl":null,"url":null,"abstract":"<div><div>With the advancement of intelligent sensing techniques, massive monitoring signals are collected and accumulated from industrial systems. Given that sensors are often correlated and constructed to reflect graph topology, the signals can be conceptualized as graph data. The polynomial filter-based graph neural networks (GNNs) are commonly employed to exploit information from nodes features and graph topology for graph data analysis. However, the polynomial filter-based GNNs encounter difficulty in accurately modeling sharp changes and the coefficients can vary a lot, making them hard to learn. To address this problem, a novel graph neural network named extended auto-regressive moving average graph neural network (eAGNN) is proposed. Compared with auto-regressive moving average (ARMA) neural network, the order restriction are removed, allowing for the inference of a more general neural network, which enables the modeling of filters with more different shapes. Furthermore, both low-frequency and high-frequency information are explicitly and separately extracted so as to alleviate the burden of the learning process and further enhance the learning capability. Finally, several experiments including public node classification and fault diagnosis were conducted. The results demonstrate that the proposed eAGNN exhibits high performance compared to alternative methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"309 ","pages":"Article 112762"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124013960","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
With the advancement of intelligent sensing techniques, massive monitoring signals are collected and accumulated from industrial systems. Given that sensors are often correlated and constructed to reflect graph topology, the signals can be conceptualized as graph data. The polynomial filter-based graph neural networks (GNNs) are commonly employed to exploit information from nodes features and graph topology for graph data analysis. However, the polynomial filter-based GNNs encounter difficulty in accurately modeling sharp changes and the coefficients can vary a lot, making them hard to learn. To address this problem, a novel graph neural network named extended auto-regressive moving average graph neural network (eAGNN) is proposed. Compared with auto-regressive moving average (ARMA) neural network, the order restriction are removed, allowing for the inference of a more general neural network, which enables the modeling of filters with more different shapes. Furthermore, both low-frequency and high-frequency information are explicitly and separately extracted so as to alleviate the burden of the learning process and further enhance the learning capability. Finally, several experiments including public node classification and fault diagnosis were conducted. The results demonstrate that the proposed eAGNN exhibits high performance compared to alternative methods.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.