Radiotekhnika
Publishing house Radiotekhnika

"Publishing house Radiotekhnika":
scientific and technical literature.
Books and journals of publishing houses: IPRZHR, RS-PRESS, SCIENCE-PRESS


Тел.: +7 (495) 625-9241

 

Application neural networks in automated information systems with factographic data

Keywords:

S. D. Kulik – Dr.Sc. (Eng.), Senior Research Scientist, Professor, National Nuclear Research University «MEPHI» (Moscow), Moscow State University of Psychology and Education (MSUPE)
K. I. Tkachenko – Head of Department, LIFEiT (Moscow). Email: konstantin.tki@gmail.com
A. A. Kondakov – Post-graduate Student, National Nuclear Research University «MEPHI» (Moscow). Email: alex.letbox@gmail.com


This paper briefly discusses the neural network and automated information systems formation of factographic data (AISFFD). AISFFD is the special class factographic systems. Method is proposed for evaluating effectiveness these systems. This method contains the necessary neural network. In article questions of construction of estimated characteristics of efficiency of technical systems by means of use neuronets algorithms are considered. Traditional methods of an estimation of efficiency mean serious expert working out of a subject domain for definition of those weight factors or other indicators, for example, at use multiplicativity a method of an estimation of efficiency what or systems. Briefly introduces the concept of building the AISFFD. The variant of a possible scheme of a specialized AISFFD for implementing it in practice are proposed. Now scientists group continue researches in this field. It is offered to use a special factographic information retrieval. This article is about automated information system of the formation factografic data (AISFFD). AISFFD is a special class of the factografic systems that help to generate factografic data with using neural network. Such system can be used in different applied tasks for generating sets of data (features) and using it for forming an object of the retrieval system. That technology lets to increase efficiency and performance of the systems using search engines of the special class or systems forming object with a sets of features. Also it can be useful for systems that work with large volumes of data. AISFFD consist several subsystems (analyze and decision-making unit, data preparing unit, factografic data generator, main factografic database, additional database, object forming system). Also AISFFD have human friendly interface that helps users (operators) to work with it without special training. In this article submitted descriptions of these subsystems and their communications, methods and algorithms that using in different units and using of the neural networks. Authors propose to use AISFFD in criminalistics systems of recreating objects by features or in a special class of retrieval systems that use features in research process. Such retrieval system use the special algorithms (differs from text retrieval systems) and can be used for searching data of different types. Now scientists group continue researches in this field. The results of the researches were successfully protected by the various security documents of ROSPATENT.
References:

  1. Galushkin A. I. Teoriya neyronnykh setey: Ucheb. posobie dlya vuzov (Neyrokomp'yutery i ikh primenenie. Kn. 1). M.: IPRZhR. 2000. 416 s.
  2. Volchikhin V. I., Ivanov A. I., Nazarov I. G., Funtikov V. A., Yazov Yu. K. Neyrosetevaya zashchita personal'nykh biometricheskikh dannykh. M.: Radiotekhnika. 2012. 160 s.
  3. Kulik S. D. Neyronnye seti v avtomatizirovannykh faktograficheskikh informatsionno-poiskovykh sistemakh // Neyrokomp'yutery: razrabotka, primenenie. 2007. № 2 – 3. S. 60 – 66.
  4. Kulik S. D., Tkachenko K. I. Issledovanie problemy raspoznavaniya obrazov na primere perseptrona s tremya vkhodami // Nauchnaya sessiya MIFI-2008. X Vserossiyskaya nauchno-tekhnich. konf. «Neyroinformatika-2008». Sbor. nauch. tr. v 2-kh ch. M.: MIFI. 2008. Ch. 1. S. 16.
  5. Kulik S. D., Nikonets D. A., Tkachenko K. I., Luk'yanov I. A. Metody i sredstva povysheniya effektivnosti informatsionnykh sistem (neyronnye seti, kriminalistika, formirovanie faktograficheskikh dannykh, morfologicheskiy analiz). T. 1: Kriminalistika. M.: Radiotekhnika (Dep. v VINITI 05.05.2011. № 206-V2011. Bibl. Ukazat. № 7 (473)). M.: 2011. 300 s.
  6. Kulik S. D., Nikonets D. A., Tkachenko K. I., Luk'yanov I. A. Metody i sredstva povysheniya effektivnosti informatsionnykh sistem (neyronnye seti, kriminalistika, formirovanie faktograficheskikh dannykh, morfologicheskiy analiz). T. 2: Sistemy / M.: Radiotekhnika. (Dep. v VINITI 05.05.2011. № 207-V2011; Bibl. Ukazat. № 7(473)). M. 2011. 223 s.
  7. Kulik S. D., Nikonets D. A., Tkachenko K. I., Luk'yanov I. A. Metody i sredstva povysheniya effektivnosti informatsionnykh sistem (neyronnye seti, kriminalistika, formirovanie faktograficheskikh dannykh, morfologicheskiy analiz). T. 3: Prilozheniya / M.: Radiotekhnika (Dep. v VINITI 05.05.2011, № 208-V2011; Bibl. Ukazat. № 7(473)). 2011. 229 s.
  8. Kulik S. D., Nikonets D. A. Primery ispol'zovaniya neyrosetevogo algoritma v metodikakh dlya eksperta-pocherkoveda // Neyrokomp'yutery: razrabotka, primenenie. 2009. № 9. S. 61 – 85.
  9. Kulik S. D., Nikonets D. A. Avtomatizatsiya kriminalisticheskogo issledovaniya rukopisnykh tekstov pri pomoshchi neyronnykh setey // Nauch. sessiya MIFI-2008. X Vseross. nauch.-tekhn. konf. «Neyroinformatika–2008». Sb. nauch. trudov v 2-kh ch. M.: MIFI. 2008. Ch. 1. S. 15.
  10. Kulik S. D. Algoritmy raspoznavaniya obrazov i modelirovanie avtomatizirovannykh faktograficheskikh informatsionno-poiskovykh sistem // Neyrokomp'yutery: razrabotka, primenenie. 2002. № 9 – 10. S. 115 – 127.
  11. Kulik S.D. Primenenie neyronnykh setey v avtomatizirovannykh faktograficheskikh informatsionno-poiskovykh sistemakh // Neyrokomp'yutery: razrabotka, primenenie. 2002. № 5 – 6. S. 3 – 12.
  12. Kulik S. D., Zhizhilev A. V. Obzor neyrosetevogo programmnogo obespecheniya dlya resheniya zadach po prognozirovaniyu sostoyaniya finansovogo rynka // Nauch. sessiya MIFI-2008. X Vseros. nauch.-tekhn. konf. «Neyroinformatika-2008». Sb. nauch. trudov v 2-kh ch. M.: MIFI. 2008. Ch. 2. S. 171.
  13. Kulik S. D., Tkachenko K. I. Razrabotka generatorov dlya obespecheniya informatsionnoy bezopasnosti // Bezopasnost' informatsionnykh tekhnologiy. 2010. №1. S.87-89.
  14. Kulik S.D., Tkachenko K.I., Nikonets D.A. Instrumental'nye sredstva vyyavleniya iskazheniy informatsii v dokumentakh // Bezopasnost' informatsionnykh tekhnologiy. 2009. № 3. S. 29 – 36.
  15. Kulik S. D., Tkachenko K. I., Luk'yanov I. A. Identifikatsiya ispolnitelya tekstov po chastotno-grammaticheskim kharakteristikam i sintaksicheskim osobennostyam // Bezopasnost' informatsionnykh tekhnologiy. 2011. № 1. S. 108 – 110.
  16. Kulik S. D., Nikonets D. A., Tkachenko K. I., Zhizhilev A. V. Ustroystvo opredeleniya poddel'nykh dokumentov // Bezopasnost' informatsionnykh tekhnologiy. 2009. № 1. S. 114 – 115.
  17. Kulik S.D., Nikonets D.A., Tkachenko K.I. Reshenie zadach kriminalistiki pri issledovanii pocherka kratkikh zapisey // Nauch. sessiya MIFI-2007. Sb. nauch. trudov v 17 t. T. 12: Informatika i protsessy upravleniya. Komp'yuternye sistemy i tekhnologii. M.: MIFI. 2007. T. 12. S. 24 – 25.
  18. Kulik S. D., Nikonets D. A., Tkachenko K. I., Luk'yanov I. A., Gun'ko N. Ye. Patent na poleznuyu model' № 111926, Rossiyskaya Federatsiya (RU), kl. MPK G 06 K 9/00. Ustroystvo opredeleniya rukopisnykh dokumentov, prinadlezhashchikh ispolnitelyu teksta na russkom yazyke. Zayavka № 2011127077/08; Zayav. 04.07.2011. Zaregistr. 27.12.2011; Prioritet ot 04.07.2011. Opubl. Byul. № 36. Ch. 4. S. 1098.
  19. Kulik S. D., Nikonets D. A., Tkachenko K. I., Zhizhilev A. V. Patent na poleznuyu model' № 73750, Rossiyskaya Federatsiya (RU), kl. MPK7 G 07 D 7/00. Ustroystvo opredeleniya fal'shivykh rukopisnykh dokumentov na russkom yazyke. Zayavka № 2007147832/22; Zayav. 25.12.2007; Zaregistr. 27.05.2008; Prioritet ot 25.12.2007. Opubl. Byul. № 15. Ch. 3. S. 860.
  20. Kulik S. D. Patent na izobretenie № 2208837, Rossiyskaya Federatsiya (RU), kl. MPK7 G 06 F 17/30. Ustroystvo dlya imitatsionnogo modelirovaniya znacheniy funktsii vykhoda avtomatizirovannoy faktograficheskoy informatsionno-poiskovoy sistemy kriminalisticheskogo naznacheniya. Zayavka № 2001129139/09; Zayav. 30.10.2001; Zaregistr. 20.07.2003; Prioritet ot 30.10.2001; Opubl. 20.07.2003; Byul. № 20. Ch. 3. S. 752 – 753.
  21. Kulik S. D. Issledovanie poiskovogo robota dlya faktograficheskogo poiska // Nauchno-tekhnicheskaya informatsiya. 2003. Ser. 2: Informatsionnye protsessy i sistemy. № 3. S. 21 – 27.
  22. Kulik S. D. Issledovanie faktograficheskikh sistem i baz dannykh // Nauchno-tekhnicheskaya informatsiya. Ser. 2: Informatsionnye protsessy i sistemy. 2003. № 4. S. 33 – 41.
  23. Kulik S. D. Faktograficheskie sistemy (metody postroeniya, modeli, strategii poiska i programmnoe obespechenie). Radiotekhnika (dep. v VINITI 23.06.2003, № 1205-V2003; Bibl. ukazat. № 8 (378), 2003). M.: 2003. 325 s.
  24. Kulik S. D. Issledovanie effektivnosti faktograficheskogo poiska v informatsionnykh sistemakh. Radiotekhnika (dep. v VINITI 29.07.2004, № 1326-V2004; Bibl. ukazat. № 9(391). 2004). M.: 2004. 251 s.
  25. Kulik S. D., Gun'ko N. Ye. Analiz psikhologicheskikh svoystv lichnosti po pocherku dlya obespecheniya informatsionnoy bezopasnosti // Bezopasnost' informatsionnykh tekhnologiy. 2012. № 3. S. 103 – 110.
  26. Kulik S. D., Nikonets D. A. Avtomatizatsiya klassifikatsionno-diagnosticheskikh pocherkovedcheskikh issledovaniy s pomoshch'yu neyronnykh setey // Informatsionnye tekhnologii. 2012. № 1. S. 70 – 75.
  27. Kulik S. D. Teoriya prinyatiya resheniy (elementy teorii proverki veroyatnykh gipotez): ucheb. posobie. M.: MIFI. 2007. 152 s.
  28. Kulik S. D. Elementy teorii prinyatiya resheniy (kriterii i zadachi): uchebnoe posobie. M.: NIYaU MIFI. 2010. 188 s.
  29. Kulik S.D., Tkachenko K.I. Otsenka effektivnosti tekhnicheskikh sistem s ispol'zovaniem neyronnykh setey // Neyrokomp'yutery: razrabotka, primenenie. 2009. № 9. S. 47 – 60.
  30. Kulik S. D., Kondakov A. A., Zyryanova O. V., Grigor'ev S. K. Svidetel'stvo na programmu Rossiyskoy Federatsii  № 2013615594. Universal specialized solver. V.1.0 (U-S-S). Zayavka № 2013613594; Zayav. 26.04.2013. Zaregistr. 17.06.2013. (ROSPATYeNT).
  31. Venttsel' Ye. S. Issledovanie operatsiy: zadachi, printsipy, metodologiya. M.: Vysshaya shkola. 2001. 208 s.
  32. Nadezhnost' i effektivnost' v tekhnike. Spravochnik v 10 t. T. 1: Metodologiya. Organizatsiya. Terminologiya. M.: Mashinostroenie. 1986. 224 s.
  33. Chernorutskiy I. G. Metody prinyatiya resheniy. Spb.: BKhV-Peterburg. 2005. 416 s.
  34. Specht D. F. Probabilistic neural networks // IEEE Transactions on Neural net-works. 1990. V. 3. № 1. R. 109 – 118.

© Издательство «РАДИОТЕХНИКА», 2004-2017            Тел.: (495) 625-9241                   Designed by [SWAP]Studio