LSTM-Based Anomaly Detection for Non-Linear Dynamical System
Anomaly detection for non-linear dynamical system plays an important role in ensuring the system stability. However, it is usually complex and has to be solved by large-scale simulation which requires extensive computing resources. In this paper, we propose a novel anomaly detection scheme in non-li...
Main Authors: | Yue Tan, Chunjing Hu, Kuan Zhang, Kan Zheng, Ethan A. Davis, Jae Sung Park |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9105007/ |
Similar Items
-
An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos
by: Waseem Ullah, et al.
Published: (2021-04-01) -
Deep Learning-Based Anomaly Detection to Classify Inaccurate Data and Damaged Condition of a Cable-Stayed Bridge
by: Hyesook Son, et al.
Published: (2021-01-01) -
Anomaly Detection using LSTM N. Networks and Naive Bayes Classifiers in Multi-Variate Time-Series Data from a Bolt Tightening Tool
by: Selander, Karl-Filip
Published: (2021) -
LSTM Networks for Detection and Classification of Anomalies in Raw Sensor Data
by: Verner, Alexander
Published: (2019) -
LSTM-Based VAE-GAN for Time-Series Anomaly Detection
by: Zijian Niu, et al.
Published: (2020-07-01)