Artificial intelligence

Scientific journal

ISSN 2710-1673

ONLINE: ISSN 2710-1681

Select your language


Modeling in the class of regression equations systems in structural uncertainty conditions

Sarychev A.1
1 The Institute of Technical Mechanics of NAS of Ukraine and SSA Ukraine

Full text (PDF)

UDC: 519.25
Publication Language: Russian
Stuc. intelekt. 2014; 19(4):14–29

Abstract: For modeling in a class of regression equations systems the system criterion of regularity with dividing of observation sample on training and testing subsamples is offered. It is proved, that the optimum set of regressors exists. The condition of a reduction of optimum system of regression equations is obtained. This condition depends on parameters of system regression equations and volumes of samples.

Keywords: uncertainty on structure of regressors, system criterion of regulatory

References:

  1. Ivakhnenko A. G. Induktivnyj metod samoorganizacii modelej slozhnykh system / A.G Ivakhnenko. – K.: Naukova dumka, 1982. – 296 s.
  2. Self-organizing methods in modelling: GMDH type algorithms / Ed. By S. J. Farlow. – New York, Basel :Marcel Decker Inc., 1984. – P. 350.
  3. Ivakhnenko A. G. Pomekhoustojchivost’ modelirovanija / A.G Ivakhnenko, V. S. Stepashko. – K. :Naukova dumka, 1985. – 216 s.
  4. Ivakhnenko A. G. Samoorganizacija prognozirujuschikh modelej / A.G Ivakhnenko, J. A. Mjuller. – K. :Tekhnika, 1985. – 223 s.
  5. Ivakhnenko A. G. Modelirovanie slozhnykh system po eksperimental’nym dannym / A.G Ivakhnenko,Uj. P. Ujrachkovskij. – M. : Radio I svjaz’, 1987. – 120 s.
  6. Madala H. R. Inductive Learning Algorithms for Complex System Modeling / H. R. Madala , A. G.Ivakhnenko. – London, Tokyo : CRC Press Inc., 1994. – 370 p.
  7. Muller J.-A. Self-organizing Data Mining. Extraсting Knowledge from Data/ J.-A. Muller, F. Lemke. – Hamburg : Libri, 2000. – 250 p.
  8. Sarychev A. P. Identifikacija sostojanij strukturno-neopredelennykh system / A. P. Sarychev. –Dnepropetrovs’k : NAN Ukrainy I NKA Ukrainy, Institut tekhnichnoj mekhaniki, 2008. – 268 s.
  9. Sovremennye metody Identifikacii sistem : per. s angl. / Eikhoff P., Vanechek A., Savaragi E., Soeda T.,Nakazimo T., Akaike H., Rajbman N., Peterka V. / Pod red. P. Eikhoff. – M. : Mir, 1983. – 400 s.
  10. Sarychev A. P. Poisk optimal’nogo mnozhestva regressorov v sisteme regressionnykh uravnenij: schemapovtornykh nablyudenij / A. P. Sarychev // Mizhnarodnyj seminar z inductivnogo modeljuvannja, 11–14lypnja 2005 r., Кyiiv : zbirnyk prac’. – Kyiiv : MNNCITIS, 2005. – S. 270–277.
  11. Sarychev A. P. Sistemnyj kriterij reguljarnosti v metode gruppovogo ucheta argumentov / A. P. Sarychev// Problemy upravlenija i informatiki. – 2006. – № 6. – S. 25–37.
  12. Sarychev A. P. Reshenie problemy razbienija v MGUA pri raschete kriterija reguljarnosti v uslovijakhaktivnogo eksperimenta / A. P. Sarychev // Avtomatika. – 1989. – № 4. – S. 19–27.
  13. Sarychev A. P. Opredelenie J-optimal’nogo mnozhestva regressorov po povtornym vyborkamnabljudenij / A. P. Sarychev // Avtomatica. – 1993. – № 3. – S. 58–66.
  14. Anderson T. Vvedenie v mnogomernyi statisticheskij analiz : per. s angl. / T. Andercon. – M. :Fizmatgiz. – 1963. – 500 s.

View full text (PDF)