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- W2170342844 abstract "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." @default.
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- W2170342844 date "2008-06-01" @default.
- W2170342844 modified "2023-10-16" @default.
- W2170342844 title "Comparative evaluation of Symbolic Dynamic Filtering for detection of anomaly patterns" @default.
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- W2170342844 doi "https://doi.org/10.1109/acc.2008.4586961" @default.
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