Online Anomaly Detection of Streaming Data for Space Payloads Based on Improved GNG Algorithm
Pages 36 - 41
Abstract
Space payloads are the important facilities for space exploration and scientific experiments. Real-time anomaly detection is the key technology for discovering the abnormal parameters and potential failures of space payloads. In this paper, we proposed a novel online anomaly detection method based on improved GNG algorithm (Growing Neural Gas), which could be online learning the dynamic character of streaming data without off-line training. Comparing to the existing traditional GNG algorithm, the proposed method could acquire better performance of online anomaly detection by means of adaptive adjustment of learning rate, adaptive addition and deletion of neurons. Finally, the experiment result indicates the improved GNG algorithm acquires better efficiency and accuracy in online anomaly detection of streaming data for space payloads.
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Index Terms
- Online Anomaly Detection of Streaming Data for Space Payloads Based on Improved GNG Algorithm
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Published In
May 2019
213 pages
ISBN:9781450371711
DOI:10.1145/3330393
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- Shenzhen University: Shenzhen University
- Sun Yat-Sen University
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Association for Computing Machinery
New York, NY, United States
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Published: 10 May 2019
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ICMSSP 2019
ICMSSP 2019: 2019 4th International Conference on Multimedia Systems and Signal Processing
May 10 - 12, 2019
Guangzhou, China
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