Comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Chinmay Rao, Soumik Sarkar, Asok Ray, Murat Yasar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2008 American Control Conference, ACC
Pages3052-3057
Number of pages6
DOIs
StatePublished - Sep 30 2008
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: Jun 11 2008Jun 13 2008

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2008 American Control Conference, ACC
CountryUnited States
CitySeattle, WA
Period6/11/086/13/08

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Rao, C., Sarkar, S., Ray, A., & Yasar, M. (2008). Comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns. In 2008 American Control Conference, ACC (pp. 3052-3057). [4586961] (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2008.4586961