Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Chinmay Rao, Asok Ray, Soumik Sarkar, Murat Yasar

Research output: Contribution to journalReview article

85 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 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.

Original languageEnglish (US)
Pages (from-to)101-114
Number of pages14
JournalSignal, Image and Video Processing
Volume3
Issue number2
DOIs
StatePublished - Feb 1 2009

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

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

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

In: Signal, Image and Video Processing, Vol. 3, No. 2, 01.02.2009, p. 101-114.

Research output: Contribution to journalReview article

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