期刊
SIGNAL IMAGE AND VIDEO PROCESSING
卷 3, 期 2, 页码 101-114出版社
SPRINGER LONDON LTD
DOI: 10.1007/s11760-008-0061-8
关键词
Symbolic dynamics; Bayesian filtering; Neural networks; Anomaly detection
资金
- U.S. Army Research Laboratory
- U.S. Army Research Office [W911NF-07-1-0376]
- U.S. Office of Naval Research [N00014-08-1-380]
- NASA [NNX07AK49A]
Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.
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