TY - GEN
T1 - Comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns
AU - Rao, Chinmay
AU - Sarkar, Soumik
AU - Ray, Asok
AU - Yasar, Murat
PY - 2008/9/30
Y1 - 2008/9/30
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 comparative evaluation of SDF 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 comparative evaluation of SDF 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 - 10.1109/ACC.2008.4586961
DO - 10.1109/ACC.2008.4586961
M3 - Conference contribution
AN - SCOPUS:52449125079
SN - 9781424420797
T3 - Proceedings of the American Control Conference
SP - 3052
EP - 3057
BT - 2008 American Control Conference, ACC
T2 - 2008 American Control Conference, ACC
Y2 - 11 June 2008 through 13 June 2008
ER -