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

3 Citations (Scopus)

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

Fingerprint

Pattern recognition
Computational efficiency
Time series
Dynamical systems
Decision making
Neural networks

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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
Rao, Chinmay ; Sarkar, Soumik ; Ray, Asok ; Yasar, Murat. / Comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns. 2008 American Control Conference, ACC. 2008. pp. 3052-3057 (Proceedings of the American Control Conference).
@inproceedings{d2a31f9fd3ac43869c7ba05a64c9fbc1,
title = "Comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns",
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.",
author = "Chinmay Rao and Soumik Sarkar and Asok Ray and Murat Yasar",
year = "2008",
month = "9",
day = "30",
doi = "10.1109/ACC.2008.4586961",
language = "English (US)",
isbn = "9781424420797",
series = "Proceedings of the American Control Conference",
pages = "3052--3057",
booktitle = "2008 American Control Conference, ACC",

}

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., 4586961, Proceedings of the American Control Conference, pp. 3052-3057, 2008 American Control Conference, ACC, Seattle, WA, United States, 6/11/08. https://doi.org/10.1109/ACC.2008.4586961

Comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns. / Rao, Chinmay; Sarkar, Soumik; Ray, Asok; Yasar, Murat.

2008 American Control Conference, ACC. 2008. p. 3052-3057 4586961 (Proceedings of the American Control Conference).

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

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.

UR - http://www.scopus.com/inward/record.url?scp=52449125079&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=52449125079&partnerID=8YFLogxK

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

ER -

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