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Mathematical Methods of Geoinformatics. II. Fuzzy-Logic Algorithms in the Problems of Abnormality Separation in Time Series

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Cybernetics and Systems Analysis Aims and scope

Abstract

Interpreter logic is simulated by means of fuzzy mathematics. Such an interpreter searches for signals on a record and recognizes signals on it. It slides along a record and locally assesses activity of small record fragments from different sides; it stores these assessments and then aggregates the whole thing into this or that single overall solution. The DRAS and FLAS algorithms are the simulation results.

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REFERENCES

  1. A. D. Gvishiani, S. M. Agayan, and Sh. R. Bogoutdinov, “Mathematical methods of geoinformatics. I. A new approach to clusterization,” Kibern. Sist. Analiz, No. 2, 104-122(2002).

    Google Scholar 

  2. A. D. Gvishiani, M. Diaman, V. O. Mikhailov et al., “Algorithms of artificial intelligence for clusterization of magnetic abnormalities,” Fizika Zemli, No. 7, 13-38 (2002).

    Google Scholar 

  3. V. O. Mikhailov, A. Galdeano, M. Diament et al., “Application of artificial intelligence for Euler solution clustering,” Geophysics, 68, No. 1, 168-180 (2003).

    Google Scholar 

  4. J. Zlotnicki, V. Mikhailov, J. L. Le Mouë el et al., “Ulf magnetic and electric signals related to volcanic activity: la Fournaise case (Reunion island),” in: 3rd Intern. Workshop on Magnetic, Electric, and Electro Magnetic Methods in Seismology and Volcanology (MEEMSV-2002) (2002), p. 127.

  5. A. Gvishiani and J. O. Dubois, Dynamic Systems and Dynamic Classification Problems in Geophysical Applications, Springer, Berlin (1998).

    Google Scholar 

  6. A. Gvishiani and J. O. Dubois, Artificial Intelligence and Dynamic Systems for Geophysical Applications, Springer, Berlin (2002).

    Google Scholar 

  7. O. K. Kedrov, L. A. Polikarpova, and G. M. Steblov, “Algorithms for detecting weak short-period seismic signals based on the time-and-frequency analysis of three-component records in real time,” Izv. RAN, Fizika Zemli, No. 8, 30-45 (1998).

    Google Scholar 

  8. N. Magotra, N. Ahmed, and E. Chael, “Seismic event detection and source location using single-station (three-component) data,” Bull. Seism. Soc. Amer., 77, 958-971 (1987).

    Google Scholar 

  9. A. Plesinger, M. Hellweg, and D. Seidl, “Interactive high-resolution polarization analysis of broadband seismogram,” J. Geophys., 59, 129-139 (1986).

    Google Scholar 

  10. B. M. Naimark, “Algorithms for detecting a seismic signal against the background of microseisms,” Vych. Seism., No. 1, 5-9 (1996).

    Google Scholar 

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Gvishiani, A.D., Agayan, S.M., Bogoutdinov, S.R. et al. Mathematical Methods of Geoinformatics. II. Fuzzy-Logic Algorithms in the Problems of Abnormality Separation in Time Series. Cybernetics and Systems Analysis 39, 555–563 (2003). https://doi.org/10.1023/B:CASA.0000003505.56410.4f

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  • DOI: https://doi.org/10.1023/B:CASA.0000003505.56410.4f

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