350 rub
Journal Neurocomputers №2 for 2014 г.
Article in number:
Scientific bases of the hierarchical systems creation for monitoring and evaluating the influence of the transport infrastructure objects on the environment
Authors:
A.N. Vasilyev - Dr.Sc. (Eng.), Professor, St.-Petersburg State Polytechnical University. E-mail: a.n.vasilyev@gmail.com
V.N. Denisov - Dr.Sc. (Eng.), Professor, St.-Petersburg Mining University. E-mail: 3565451@mail.ru
D.A. Thakhov - Dr.Sc. (Eng.), Professor, St.-Petersburg State Polytechnical University. E-mail: dtarkhov@gmail.com
V.N. Fedotov - Associate Professor, St.-Petersburg Mining University. E-mail: nik2k@mail.ru
Abstract:
Ecology worsening of megalopolises leads to the need for the solution to the problem of constructing the hierarchical intellectual system of monitoring and predicting the influence of motor transport and objects of transport infrastructure on the environment, and progress in the field of information-computing technologies makes the solution of this problem possible. In the publication scientific bases  methods and algorithms  are proposed for the construction of such hierarchical intellectual systems on the basis of the neural networks models. These models are constantly adjusted to updatable data and sent from one level of hierarchy to another one. Isolation of the control parameters in the three-level hierarchical information system and their optimum selection will make it possible to reduce ecological backlash from the transport infrastructure on the environment. It is important to create a set of the neural network models of the standard urban elements (templates «Main street», «Canyon», «Cross-road» and other), which allow refinement in the process of construction and functioning on the basis of real measurements. The basis of project is created by the authors universal approach to the construction of the hierarchy of neural networks models for each of the cases: «equations» or «data sets»,  and in the mixed heterogeneous situation: «equations + data». Using this approach it is possible to build resistant to the errors and capable of learning new pieces of information neural network models.
Pages: 22-30
References

  1. Marchuk G.I. Matematicheskoe modelirovanie v probleme okruzhayushhej sredy'. M.: Nauka.1982. 320 s.
  2. Vasilyev A.N., Tarkhov D.A. New neural network technique to the numerical solution of mathematical physics problems. I: Simple problems // Optical Memory and Neural Networks (Information Optics), Allerton Press, Inc. 2005. V. 14. №. 1. P. 59(72.
  3. Vasilyev A.N., Tarkhov D.A. New neural network technique to the numerical solution of mathematical physics problems. II: Complicated and nonstandard problems // Optical Memory and Neural Networks (Information Optics), Allerton Press, Inc. 2005. V. 14. №. 2. P. 97(122.
  4. Tarxov D.A. Nejronny'e seti kak sredstvo matematicheskogo modelirovaniya // Nejrokomp'yutery': razrabotka, primenenie. 2006. № 2. S. 3(48.
  5. Vasil'ev A.N. Matematicheskoe modelirovanie raspredelenny'x sistem s pomoshh'yu nejronny'x setej // Mat. modelirovanie. 2007. T. 19. № 12. S. 32(42.
  6. Vasil'ev A.N., Tarxov D.A. Postroenie priblizhenny'x nejrosetevy'x modelej po raznorodny'm danny'm // Mat. modelirovanie. 2007. T. 19.  № 12. S. 43(51.
  7. Vasil'ev A.N., Tarxov D.A. Nejrosetevoe modelirovanie. Princzipy'. Algoritmy'. Prilozheniya. SPb.: Izd-vo SPbGPU. 2009.
  8. Vasil'ev A.N., Osipov V.P., Tarxov D.A. Unificzirovanny'j proczess modelirovaniya fiziko-texnicheskix ob''ektov s raspredelenny'mi parametrami// Nauchno-texn. vedomosti SPbGPU. «Fiz.-mat. nauki». 2010.  № 3(104). S. 39(52.
  9. Vasil'ev A.N. Porubaev F.V., Tarxov D.A. Nejrosetevoj podxod k resheniyu nekorrektny'x zadach teploperenosa // Nauchno-texn. vedomosti SPbGPU. «Informatika. Telekommunikaczii. Upravlenie». 2011. № 1(115). S. 133(142.
  10. Vasil'ev A.N., Porubaev F.V., Tarxov D.A. Nejrosetevoe reshenie dvumernoj obratnoj zadachi teploperenosa s tochechny'mi danny'mi izmerenij // Nejrokomp'yutery': razrabotka, primenenie. 2011. № 6. S. 38(44.
  11. Vasil'ev A.N., Tarxov D.A. O nejrosetevom podxode k postroeniyu priblizhenny'x reshenij prikladny'x zadach v klassicheskoj i neklassicheskoj postanovkax / Sovremenny'e informaczionny'e texnologii i IT-obrazovanie. Sb. nauch. trudov VII Mezhdunar. nauchno-prakt. konf. M.: MGU. 2012. T.1. S. 310(321. 
  12. Vasil'ev A.N., Osipov V.P., Tarxov D.A. Unificzirovanny'j proczess modelirovaniya sistem s raspredelenny'mi parametrami // Nejrokomp'yutery': razrabotka, primenenie. 2010. № 7. S. 20(28.
  13. Vasil'ev A.N., Tarxov D.A. Parametricheskie nejrosetevy'e modeli klassicheskix i neklassicheskix zadach dlya uravneniya teploprovodnosti // Nauchno-texnicheskie vedomosti SPbGPU. «Fiz.-matem. nauki. 2012. № 3(153). S. 136(144.
  14. Vasil'ev A.N., Osipov V.P. Konczepcziya i princzipy' raczional'noj sxemy' prikladnogo modelirovaniya // Nejrokomp'yutery': razrabotka, primenenie. 2011. № 6. S. 29(37.
  15. Vasil'ev A.N., Tarxov D.A. Matematicheskie modeli sistem s interval'no zadanny'mi parametrami na osnove geterogenny'x nejronny'x setej. Prodolzhenie temperaturnogo polya - klassicheskaya postanovka zadachi // Nejrokomp'yutery': razrabotka, primenenie. 2012. № 11. S. 56(59.
  16. Denisov V.N., Rogalev V.A. Problemy' e'kologizaczii avtomobil'nogo transporta. 2007.
  17. Fedotov V.N. Snizhenie riska e'kologicheskogo vozdejstviya avtotransporta - kriterij upravleniya dorozhny'm dvizheniem. M.: NPP Transnavigacziya. Mintrans Rossii. 2008.
  18. Fedotov V.N., Gudkov V.A., Komarov Ju.Ja. Metodologiya aktivnogo vozdejstviya na e'kologicheskuyu nagruzku gorodskogo avtotransporta (monografiya). Volgograd: Tipografiya UINL VolgGTU. 2009.
  19. Fedotov V.N. Vy'bor ob''ekta i algoritma nejroprogramm koordinaczii sistem upravleniya dorozhny'm dvizheniem po kriteriyu riska e'kologicheskogo vozdejstviya. M.: NAUChTEXLITIZDAT. 2010.
  20. Fedotov V.N. Inzhenerny'j metod ochistki vozdushnoj sredy' gorodskoj UDS. SPb.: Izd-vo nauchno-issled. in-a. oxrany' atm. vozduxa (NII Atmosfera). 2011.
  21. Fedotov V.N. E'ksperimental'no-analiticheskoe opredelenie skorosti rasprostraneniya poverxnosti fronta zagryaznyayushhix veshhestv avtotransportnogo potoka. SPb.: RIO SPbGASU. 2012.
  22. Fedotov V.N. Model' rasseivaniya zagryaznyayushhix veshhestv v vozduxe gorodskoj avtomagistrali. SPb.: Izd-vo nauchno-issled. in-a oxrany' atm. vozduxa (NII Atmosfera). 2012.
  23. Denisov V.N. Mikroskopicheskie tverdy'e chasticzy' kak prioritetny'j vid zagryaznenij v megapolisax Rossii. SPb.: Izd-vo SPbGU ITMO. 2012.
  24. Krivonozhko V.E., Rozhnov A.V., Ly'chev A.V. Postroenie gibridny'x intellektual'ny'x informaczionny'x sred i komponentov e'kspertny'x sistem na osnove obobshhyonnoj modeli analiza sredy' funkczionirovaniya // Nejrokomp'yutery': razrabotka, primenenie. 2013. № 6. S. 3-12.