The cognitive technology of intelligent control: soft computing optimizer and deep machine learning
Nečetkie sistemy i mâgkie vyčisleniâ, Tome 13 (2018) no. 2, pp. 166-182.

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

The article discusses the possibility of using the deep machine learning technology based on the soft computing optimizer in cognitive intelligent control using a cognitive helmet as a neural interface. The goal of this work is to experimentally show the possibility of classifying the mental states of a human operator, to identify objective indicators of the psychophysiological state of the examined person. The role and necessity of applying intelligent calculations in the task of describing the general psychophysical state of an operator is shown with examples.
Keywords: neural interface, intelligent computing, intelligent control system, deep machine learning, emotions.
@article{FSSC_2018_13_2_a4,
     author = {S. V. Ul'yanov and A. A. Mamaeva and A. V. Schevchenko},
     title = {The cognitive technology of intelligent control: soft computing optimizer and deep machine learning},
     journal = {Ne\v{c}etkie sistemy i m\^agkie vy\v{c}isleni\^a},
     pages = {166--182},
     publisher = {mathdoc},
     volume = {13},
     number = {2},
     year = {2018},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/FSSC_2018_13_2_a4/}
}
TY  - JOUR
AU  - S. V. Ul'yanov
AU  - A. A. Mamaeva
AU  - A. V. Schevchenko
TI  - The cognitive technology of intelligent control: soft computing optimizer and deep machine learning
JO  - Nečetkie sistemy i mâgkie vyčisleniâ
PY  - 2018
SP  - 166
EP  - 182
VL  - 13
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/FSSC_2018_13_2_a4/
LA  - ru
ID  - FSSC_2018_13_2_a4
ER  - 
%0 Journal Article
%A S. V. Ul'yanov
%A A. A. Mamaeva
%A A. V. Schevchenko
%T The cognitive technology of intelligent control: soft computing optimizer and deep machine learning
%J Nečetkie sistemy i mâgkie vyčisleniâ
%D 2018
%P 166-182
%V 13
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/FSSC_2018_13_2_a4/
%G ru
%F FSSC_2018_13_2_a4
S. V. Ul'yanov; A. A. Mamaeva; A. V. Schevchenko. The cognitive technology of intelligent control: soft computing optimizer and deep machine learning. Nečetkie sistemy i mâgkie vyčisleniâ, Tome 13 (2018) no. 2, pp. 166-182. http://geodesic.mathdoc.fr/item/FSSC_2018_13_2_a4/

[1] Petrov B. N., Ulanov G. M., Ulyanov S. V., Khazen E. M., Information and semantic problems in management and organization processes, Nauka Publ., Moscow, 1977, 452 pp. (in Russian)

[2] Noor A. K., “Potential of cognitive computing and cognitive systems”, Open Engineering, 5:1 (2015), 75–88 | DOI

[3] Hieida C., Horii T., Nagai T., Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks, Computing Research Repository, 2018 http://arxiv.org/abs/1808.08447

[4] Rozaliev V. L., “Construction of a mathematical model of emotions”, Proceedings of the V international scientific-practical conference “Integrated models and soft computing in artificial intelligence” (Kolomna, 28-30 maya 2009 g.), v. 2, Fizmatlit Publ., Moscow, 950–957 (in Russian)

[5] Ulyanov S. V., Litvintseva L. V., Dobrynin V. N., Mishin A. A., Intelligent Robust Control: Soft Computing Technologies, VNIIgeosistem Publ., Moscow, 2011, 406 pp. (in Russian)

[6] Ulyanov S. V., Reshetnikov A. G., Mamaeva A. A., Skotnikov S. V., “Hybrid cognitive control systems on the example of driving”, System analysis in science and education, 2016, no. 2 (in Russian) http:/www.sanse.ru/archive/40

[7] Fretska E., Bauer H. U., Leodolter M., Leodolter U., “Loss of control and negative emotions: a cortical slow potential topography study”, International Journal of Psychophysiology, 1999, no. 33, 127–141 | DOI

[8] Nikolaev A. R., “Spectral characteristics of the EEG at the first stage of solving various spatial problems”, Psychological Journal, 15:6 (1994), 100–106 (in Russian)

[9] Ulyanov S. V., Mamaeva A. A., Shevchenko A. V., “Cognitive-intellectual system of diagnostics, training and adaptation of children with autism”, System analysis in science and education, 2016, no. 5 (in Russian) http:/www.sanse.ru/archive/42

[10] Lapshina T. N., “Psychophysiological diagnosis of human emotions in terms of EEG”, Materials of the International Scientific and Practical Conference “Development of the Scientific Heritage of Boris Mikhailovich Teplov in the Russian and World Science” (Moscow, November 15-16, 2006), BF «Tverdislov», Moscow, 160–165 (in Russian)

[11] Ulyanov S. V., Soft computing optimizer of intelligent control system structures, US Patent No 7,219,087B2. Date of patent: May 15, 2007 [WO 2005/013019 A3, 2005]

[12] Ulyanov S. V., Reshetnikov A. G., Mamaeva A. A., “Hybrid cognitive fuzzy control systems for an autonomous robot based on neurointerface and soft computing”, Software and Systems, 30:3 (2017), 420–424 (in Russian) | DOI | MR