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Journal Neurocomputers №4 for 2013 г.
Article in number:
Trained Markov models to optimize the order of tasks in psychological testing
Authors:
L.S. Kuravsky, A.A. Margolis, P.A. Marmalyuk, G.A. Yuryev, P.N. Dumin
Abstract:
The concept of a decision support system designed to optimize the order of tasks during psychological testing and based on trained continuous-time Markov models is presented. Diagnostic conclusions are derived using probabilistic estimates of being in different subject-s classes. These estimates are improved during testing procedure. Selection of a regular task is carried out for each subject individually, with previous testing outcome and forecasting the discriminating fineness of future tasks being in use.
Pages: 28-38
References
  1. Galushkin A.I. Nejjronnye seti. Osnovy teorii. M.: Gorjachaja linija - Telekom. 2010.
  2. Golovko V.A. Nejjronnye seti: obuchenie, organizacija i primenenie. Ucheb. posobie. M.: IPRZHR. 2001.
  3. Dzhekson P. Vvedenie v ehkspertnye sistemy: Ucheb. posobie. M.: Viljams. 2001.
  4. Dzhons M.T. Programmirovanie iskusstvennogo intellekta v prilozhenijakh. M.: DMK Press. 2004.
  5. Kramer G. Matematicheskie metody statistiki. M.: Mir. 1976. 648 s.
  6. Kuravskijj L.S., Baranov S.N. Primenenie nejjronnykh setejj dlja diagnostiki i prognozirovanija ustalostnogo razrushenija tonkostennykh konstrukcijj // Nejjrokompjutery: razrabotka i primenenie. 2001. № 12. S. 47-63.
  7. Kuravskijj L.S., Baranov S.N. Sintez setejj Markova dlja prognozirovanija ustalostnogo razrushenija // Nejjrokompjutery: razrabotka, primenenie. 2002. № 11. S. 29-40.
  8. Kuravskijj L.S., Baranov S.N., Kornienko P.A.Obuchaemye mnogofaktornye seti Markova i ikh primenenie dlja issledovanija psikhologicheskikh kharakteristik // Nejjrokompjutery: razrabotka, primenenie. 2005. № 12. S. 65-76.
  9. Kuravskijj L.S., Baranov S.N., JUrev G.A.Sintez i identifikacija skrytykh markovskikh modelejj dlja diagnostiki ustalostnogo razrushenija // Nejjrokompjutery: razrabotka, primenenie. 2010. № 12. S. 20-36.
  10. Kuravskijj L.S., Margolis A.A., JUrev G.A.Psikhologicheskijj trening na osnove nejjrosetevojj tekhnologii // Nejjrokompjutery: razrabotka, primenenie. 2009. № 9. S. 20-26.
  11. Kuravskijj L.S., JUrev G.A. Ispolzovanie markovskikh modelejj pri obrabotke rezultatov testirovanija // Voprosy psikhologii. 2011. № 2. S. 98-107.
  12. Ljuger Dzh. F. Iskusstvennyjj intellekt: strategii i metody reshenija slozhnykh problem. Izd. 4-e. Per. s angl. M.: Viljams. 2003.
  13. Ovcharov L.A. Prikladnye zadachi teorii massovogo obsluzhivanija. M.: Mashinostroenie. 1969. 324 c.
  14. Psikhodiagnostika v Rossii cherez 5 let // Psikhologija. ZHurnal Vysshejj shkoly ehkonomiki. 2008. № 4. T. 5. S. 44-85.
  15. Saati T.L. EHlementy teorii massovogo obsluzhivanija i ejo prilozhenija. M.: LIBROKOM. 2010. 520 s.
  16. Kuravsky L.S., Baranov S.N. Condition monitoring of the structures suffered acoustic fatigue failure and forecasting their service life. Proc. Condition Monitoring 2003, Oxford, United Kingdom. July 2003. P. 256-279,
  17. Kuravsky L.S., Baranov S.N. Neural networks in fatigue damage recognition: diagnostics and statistical analysis. Proc. 11th International Congress on Sound and Vibration, St.-Petersburg, Russia. July 2004. P. 2929-2944,
  18. Kuravsky L.S., Baranov S.N. The concept of multifactor Markov networks and its application to forecasting and diagnostics of technical systems. In: Proc. Condition Monitoring 2005. Cambridge, United Kingdom. July 2005. P. 111-117.
  19. Kuravsky L.S., Baranov S.N., Yuryev G.A. Synthesis and identification of hidden Markov models based on a novel statistical technique in condition monitoring. In: Proc. 7th International Conference on Condition Monitoring & Machinery Failure Prevention Technologies, Stratford-upon-Avon, England. June 2010.
  20. http:// www.solver.com.