Geodesic
Choice of parameters for neuronetwork model
News of the Kabardin-Balkar scientific center of RAS, no. 1 (2011), pp. 118-121.

Voir la notice de l'article provenant de la source Math-Net.Ru

The possibility of Kohonen’s Self Organizing Maps (SOM) implementation for construction of dynamics objects predictive models for technological process monitoring is considered. The short review of fractal dimensionality determination methods is observed. Method of fractal dimensionality determination is used for finding optimal parameters of SOM. Problem of determination of butt joint’s quality in the argon-arc welding technological process has been analyzed by neuro-regressional model.
Keywords: dynamic objects, predictive models, time series fractal dimensionality, Kohonen Self Organizing Maps, training algorithm. 
@article{IZKAB_2011_1_a20,
     author = {P. V. Evdokimov},
     title = {Choice of parameters for neuronetwork model},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {118--121},
     publisher = {mathdoc},
     number = {1},
     year = {2011},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2011_1_a20/}
}
TY  - JOUR
AU  - P. V. Evdokimov
TI  - Choice of parameters for neuronetwork model
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2011
SP  - 118
EP  - 121
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2011_1_a20/
LA  - ru
ID  - IZKAB_2011_1_a20
ER  - 
%0 Journal Article
%A P. V. Evdokimov
%T Choice of parameters for neuronetwork model
%J News of the Kabardin-Balkar scientific center of RAS
%D 2011
%P 118-121
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2011_1_a20/
%G ru
%F IZKAB_2011_1_a20
P. V. Evdokimov. Choice of parameters for neuronetwork model. News of the Kabardin-Balkar scientific center of RAS, no. 1 (2011), pp. 118-121. http://geodesic.mathdoc.fr/item/IZKAB_2011_1_a20/

[1] T. Kohonen, Self-Organization, Associative Memory, Springer-Verlag, New-York, 1988, 620 pp. | MR | Zbl

[2] A. I. Gavrilov, P. V. Evdokimov, “Primenenie setei Kokhonena v zadachakh avtomatizi rovannogo strukturirovaniya i analiza informatsii”, Trudy pyatogo mezhdunarodnogo simpoziuma «Intellektualnye sistemy», MGTU im. N.E. Baumana, M, 2002, 315–317 (Kaluga)

[3] P. V. Evdokimov, “g”, Samoorganizuyuschiesya karty Kokhonena v zadache identifikatsii ne lineinykh sistem, Moskva-Dolgoprudnyi, 2006, 175–176

[4] P. V. Evdokimov, “Neirosetevye prognoziruyuschie modeli dinamicheskikh ob'ektov”, Materialy vtoroi mezhdunarodnoi konferentsii «Modelirovanie ustoichivogo regio nalnogo razvitiya», v. Tom. III. Sektsionnye doklady, Nalchik, 2007, 209–212

[5] P. V. Evdokimov, “Vybor optimalnykh parametrov neiroregressionnoi modeli tekhnologicheskogo protsessa”, Materialy tretei mezhdunarodnoi konferentsii «Modelirovanie ustoichivogo regionalnogo razvitiya», v. Chast II. Sektsionnye doklady, Nalchik, 2009, 209–213

[6] A. M. Dubrov, V. S. Mkhitaryan, L. I. Troshin, Mnogomernye statisticheskie metody, uchebnik, Finansy i statistika, M., 1998, 352 pp.

[7] A. I. Chulichkov, Matematicheskie modeli nelineinoi dinamiki, 2-e izd., ispr., FIZMATLIT, M., 2003, 296 pp. | MR

[8] G. G. Malenitskii, A. B. Potapov, Sovremennye problemy nelineinoi dinamiki, Izd. 2-e, ispravl. i dop., Editorial URSS, M., 2002, 360 pp.

[9] E. A. Gladkov, Avtomatizatsiya svarochnykh protsessov, v. Ch. I, II, MVTU im. N.E. Baumana, M., 1976, 176 pp.