TY - JOUR
T1 - Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns
AU - Rao, Chinmay
AU - Ray, Asok
AU - Sarkar, Soumik
AU - Yasar, Murat
N1 - Funding Information:
This work has been supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant No. W911NF-07-1-0376, by the U.S. Office of Naval Research under Grant No. N00014-08-1-380, and by NASA under Cooperative Agreement No. NNX07AK49A.
PY - 2009/2
Y1 - 2009/2
N2 - 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.
AB - 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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U2 - 10.1007/s11760-008-0061-8
DO - 10.1007/s11760-008-0061-8
M3 - Review article
AN - SCOPUS:70350662964
SN - 1863-1703
VL - 3
SP - 101
EP - 114
JO - Signal, Image and Video Processing
JF - Signal, Image and Video Processing
IS - 2
ER -