RUSSIAN JOURNAL OF EARTH SCIENCES VOL. 10, ES1001, doi:10.2205/2007ES000278, 2008
[2] Generally accepted algorithms used for the identification of anomalies from records of signals are mostly based on statistical and frequency-time analyses. Presently, approaches to the solution of this problem involve the use of artificial intelligence, and this direction of research is the subject of the present paper addressing the fuzzy comparison algorithm for recognition of signals (FCARS). The algorithm is a result of soft (based on fuzzy logic) modeling of the logic of an interpreter attempting to detect anomalies in signal records. We utilized the formulation of such a logic proposed by Neimark [1966] for its "probabilistic'' modeling.
Citation: 2008), Recognition of anomalies from time series by fuzzy logic methods, Russ. J. Earth Sci., 10, ES1001, doi:10.2205/2007ES000278.
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