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1

International Workshop on Epileptic Seizure Prediction (3rd 2007 Freiburg im Breisgau, Germany). Seizure prediction in epilepsy: From basic mechanisms to clinical applications. Weinheim: Wiley-VCH, 2008.

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2

United States. National Telecommunications and Information Administration., ed. Medium frequency propagation prediction techniques and antenna modeling for Intelligent Transportation Systems (ITS) broadcast applications. [Boulder, Colo.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1999.

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3

DeMinco, N. Medium frequency propagation prediction techniques and antenna modeling for Intelligent Transportation Systems (ITS) broadcast applications. [Boulder, Colo.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1999.

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4

United States. National Telecommunications and Information Administration, ed. Medium frequency propagation prediction techniques and antenna modeling for Intelligent Transportation Systems (ITS) broadcast applications. [Boulder, Colo.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1999.

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5

Center, Langley Research, ed. Empirical modeling of environment-enhanced fatigue crack propagation in structural alloys for component life prediction. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1995.

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6

Automotive model predictive control: Models, methods and applications. Berlin: Springer, 2010.

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7

McMillan, Gregory K. Models unleashed: Virtual plant and model predictive control applications : a pocket guide. Research Triangle Park, NC: ISA, 2004.

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8

Phillips, Peter C. B. Bayesian model selection and prediction with empirical applications. New Haven, CN: Yale University, Cowles Foundation, 1992.

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9

James, Judge W., Sebastian Lynne, and Altschul Jeffrey H, eds. Quantifying the present and predicting the past: Theory, method, and application of archeological predictive modeling. Denver, Colo: U.S. Dept. of the Interior, Bureau of Land Management, 1988.

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10

United States. National Aeronautics and Space Administration., ed. Modeling improvements and users manual for axial-flow turbine off-design computer code AXOD. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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11

1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.

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12

Camacho, E. F. Model predictive control. London: Springer, 2003.

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13

Camacho, E. F. Model predictive control. 2nd ed. New York: Springer, 2004.

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14

Grancharova, Alexandra. Explicit Nonlinear Model Predictive Control: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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15

service), SpringerLink (Online, ed. Nonlinear model predictive control: Towards new challenging applications. Berlin: Springer, 2009.

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16

1945-, McMeekin T. A., ed. Predictive microbiology: Theory and application. Taunton, Somerset, England: Research Studies Press Ltd., 1993.

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17

O'Muircheartaigh, I. G. Prediction of polytomous events: Model description, algorithm development and methodological aspects, with an application. Monterey, Calif: Naval Postgraduate School, 1987.

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18

Radhakrishnan, V. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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19

Sreeramesh, Kalluri, Halford Gary R, and United States. National Aeronautics and Space Administration., eds. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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20

Sreeramesh, Kalluri, Halford Gary R, and United States. National Aeronautics and Space Administration., eds. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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21

Radhakrishnan, V. Application of an energy-based life prediction model to bithermal and thermomechanical fatigue. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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22

Matthew, O'Keefe, Kerr Christopher, United States. Dept. of Energy. Office of Biological and Environmental Research., and Goddard Space Flight Center, eds. Second International Workshop on Software Engineering and Code Design in Parallel Meteorological and Oceanographic Applications: Proceedings of a workshop sponsored by the U.S. Department of Energy, Office of Biological and Environmental Research; the Department of Defense, High Performance Computing and Modernization Office; and the NASA Goddard Space Flight Center, Seasonal-to-Interannual Prediction Project, and held at the Camelback Inn, Scottsdale, Arizona, June 15-18, 1998. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.

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23

Bousfield, Wayne E. Application of predictive model to forecast Douglas-fir tussock moth defoliation. Missoula, Mont: USDA Forest Service, Northern Region, 1986.

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24

C, Georgiadis Michael, Pistikopoulos Efstratios N, and Dua Vivek, eds. Multi-parametric model-based control: Theory and applications. Weinheim: Wiley-VCH, 2007.

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25

Konnie, Wescott, and Brandon R. Joe 1967-, eds. Practical applications of GIS for archaeologists: A predictive modeling toolkit. London: Taylor and Francis, 2000.

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26

Drew, C. Ashton. Predictive species and habitat modeling in landscape ecology: Concepts and applications. New York: Springer, 2011.

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27

V, Kaza K. R., and United States. National Aeronautics and Space Administration., eds. Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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28

Georgiades, P. S. Application of a non-linear k-w model to the prediction of by-pass transition. Manchester: UMIST, 1995.

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29

V, Kaza K. R., and United States. National Aeronautics and Space Administration., eds. Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter. [Washington, DC]: National Aeronautics and Space Administration, 1992.

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30

Koch, J. Estimation of uncertainties in predictions of environmental transfer models: Evaluation of methods and application to CHERPAC. Chalk River, Ont: Environmental Research Branch, Chalk River Laboratories, 1995.

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31

Pasiouras, Fotios. Application of quantitative techniques for the prediction of bank acquisition targets. Singapore: World Scientific, 2005.

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32

Application of quantitative techniques for the prediction of bank acquisition targets. Singapore: World Scientific, 2006.

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33

(Korea), Han'gang Hongsu T'ongjeso. Kangsu yech'ŭk charyo ŭi hwaryong pangan maryŏn =: Application of rainfall forecasts on numerical weather prediction model. Kyŏnggi-do Kwach'ŏn-si: Kukt'o Haeyangbu Han'gang Hongsu T'ongjeso, 2010.

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34

R, Halford Gary, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Application of thermal life prediction model to high-temperature aerospace alloys B1900+Hf and Haynes 188. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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35

J, Shamroth S., Langley Research Center, and Scientific Research Associates, eds. On the application of a hairpin vortex model of wall turbulence to trailing edge noise prediction. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1985.

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36

Ahmad, Anees. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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37

Zhen, Feng, and United States. National Aeronautics and Space Administration., eds. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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38

Chen, Feng, and United States. National Aeronautics and Space Administration., eds. Software to model AXAF-I image quality: Final report. [Washington, DC: National Aeronautics and Space Administration, 1995.

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39

Parlia, Sean. Ion-Pair Conductivity Model and Its Application for Predicting Conductivity in Non-Polar Systems. [New York, N.Y.?]: [publisher not identified], 2020.

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40

United States. National Aeronautics and Space Administration., ed. An RL10A-3-3A rocket engine model using the rocket engine transient simulator (ROCETS) software. [Washington, D.C.]: National Aeronautics and Space Administration, 1993.

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41

National Academies Press (U.S.), ed. Applications of toxicogenomic technologies to predictive toxicology and risk assessment. Washington, D.C: National Academies Press, 2007.

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42

United States. National Aeronautics and Space Administration., ed. Improvement And Application Of Atmospheric Radiative Transfer Models For Prediction Of Final Report... NASA/CR-1998-208218... Aug. 6, 1998. [S.l: s.n., 1999.

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43

Austin, Richard Arthur. The development of a multi-tide tidal energy prediction model and its application to the proposed Mersey Barrage. Salford: University of Salford, 1990.

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44

Zhao, Shichang. Development of a kinetic geochemical model and its application in the prediction of acid mine drainage associated with mining. University Park, PA: Pennsylvania State University, 1998.

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45

Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms. Meets the requirements of Federal state educational standards of higher education of the last generation. For students of economic specialties, specialists, and graduate students.
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46

Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models: Theory and Applications. Springer, 2019.

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47

Jain, Madhu, Kusum Deep, and Said Salhi. Performance Prediction and Analytics of Fuzzy, Reliability and Queuing Models: Theory and Applications. Springer, 2018.

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48

Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications. Wiley-VCH, 2008.

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49

Timmer, Jens, Andreas Schulze-Bonhage, and Bjö Schelter. Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications. Wiley & Sons, Incorporated, John, 2008.

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50

Cameron, Robert Andrew Duncan, and Gregory K. McMillan. Models Unleashed: Virtual Plant and Model Predictive Control Applications. ISA: The Instrumentation, Systems, and Automation Society, 2003.

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