Issue 1 (191), article 3


Kibern. vyčisl. teh., 2018, Issue 1 (191), pp.

Grytsenko V.I., Corresponding Member of NAS of Ukraine,
Director of International Research and Training
Center for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine
Volkov О.Y., Senior Researcher,
Intellectual Control Department
Komar M.M., Researcher,
Intellectual Control Department
Bogachuk Y.P., PhD (Engineering), Senior Researcher,
Intellectual Control Department
International Research and Training Center for Information
Technologies and Systems of the National Academy
of Sciences of Ukraine and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., 03187, Kiev, Ukraine


Introduction. The article discusses the actual questions of the need of creation of modern systems of automatic control of unmanned aerial vehicle (UAV) and describes new methods of its intellectualization. Today’s development of information technology requires accelerated development of the theory of intellectual control and the theory of systemic information technology. New technologies of intellectual control are extremely important for solving the problems of modern unmanned aviation.
The purpose of the article is to solve the issues of the development of the control system of UAV and to provide a number of measures aimed to ensuring its intellectualization. The approach considered in the article is based on the theory of high-precision remote control of dynamic objects and on the complex interaction of methods of theory of invariance, adaptive control and intellectualization of processes of UAV control.
Results. The development and implementation of control algorithms using functional program modules written in modern high-level programming languages in the computer environment based on microprocessors with a computing power sufficient to implement these algorithms in the form of a unified hardware and software complex of the integrated avionics.
The expansion of the range of functional capabilities of UAV control system that is offered to supplement the developed channels and algorithms of autopilot by the methods of intellectualization.
Conclusions. It is shown that combining the developed control laws for UAV autopilot into a unified hardware and software complex of integrated avionics and supplementing them with the proposed components of intellectualization will create a synergy effect and ensure the effectiveness and sustainability of the process of controlling the motion of the UAV.

Keywords: unmanned aerial vehicle, control system, invariance, intellectualization,

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Received 27.12.2017

Issue 4 (190), article 1


Kibern. vyčisl. teh., 2017, Issue 4 (190), pp.

Grytsenko V.I., Corresponding Member of NASU of Ukraine,
Director of International research and training
center for Information technologies and systems
of the NASU and MESU
Onyshchenko I.M., PhD (Economics),
Senior Researcher of the Department of Economic and Social
Systems and Information Technologies
International research and training center for Information
technologies and systems of the NASU and MESU
40, Ave Glushkov, 03680, Kiev, Ukraine


Introduction. Fast growth of collected and stored data due to IT bumming caused a problem called “Big Data Problem”. Most of the new data are unstructured and this is the core reason why traditional relational data warehouse are so inefficient to deal with “Big Data”. Predicting and modeling based on “Big Data” also can be problematic because of high volume and velocity. To avoid some problems online learning algorithms can be successful for high-load systems.
The purpose of the article is to develop an approach to feature selection and modeling in case of “Big Data” with using online learning algorithm.
Method. Online learning algorithm for FTRL (Follow-The-Regularized-Leader) model with L1 and L2 regularization to select only important features was used.
Results. The approaches of modeling in cases of using batch and online learning algorithms are described on the example of online auction system. The online learning algorithm has very strong preferences in case of high load and high velocity. Mathematical background for modification of linear discriminator of FTL (Follow-The-Leader) model with adding regularization was described. L1 and L2 regularization allows us to select important features in real time. If the feature becomes useless, the regularization will set the corresponding coefficient equal to 0. But it does not remove the feature from training process and the coefficient can be restored with some value in case of its importance for model. The full process is prepared as a program in Python and can be used in practice.
The results may be applied for modeling and predicting in projects with high volume or velocity of data for example — social networks, online auctions, online gaming, recommendation systems and others.
The results may be applied for modeling and forcasting in projects with high volume or velocity of data, for example — social networks, online auctions, online gaming, recommendation systems and others .
Conclusions. FTRL model to work as online learning algorithm that allows to predict binary outcomes in high load “Big Data” systems was modified.
Getting into account that number of predictors can be enormous it takes much computing resources, time and make the process difficult. This feature selection problem was solved with using L1 regularization. The selection procedure was added to modified online learning FTRL model. L1 regularization to score the importance of predictors in real time was used.
A program that runs described mathematical algorithm was developed. Note that the algorithm effectively works with sparse matrices by analyzing incoming data and updating weights only for predictors that are presented. The algorithm has L1 and L2 regularization features that may be used for feature selection and avoid overfitting.
Keywords: information technologies in economics, economical and mathematical modeling, online learning algorithms, regularization, Big Data.

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1 Maier-Shenberher Vyktor. Bolshye dannye. Revoliutsyia, kotoraia yzmenyt to, kak my zhyvem, rabotaem i myslym/Vyktor Maier-Shenberher, Kennet Kuker; per. s anhl. Ynna Haidiuk. — Moskow: Mann, Yvanovy Ferber, 2014. — 240 p. (in Russian).

2 M. Regelson and D. Fain. Predicting click-through rate using keyword clusters. In Proceedings of the Second Workshop on Sponsored Search Auctions, volume 9623. Citeseer, 2006.

3 M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, pages 521–530. ACM, 2007.

4 Shalev-Shwartz, Shai. “Online Learning and Online Convex Optimization”. Foundations and Trends in Machine Learning. 2011. pp. 107–194.

5 Gilles Gasso. Batch and online learning algorithms for nonconvex Neyman-Pearson classification / Gilles Gasso, Aristidis Pappaioannou, Marina Spivak, Leon Bottou / ACM Transaction on Intelligent System and Technologies, 2(3), 2011.

6 H Brendan McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization. International Conference on Artificial Intelligence and Statistics, pages 525–533, 2011.

7 Byll Franks. Ukroshchenye bolshykh dannykh: kak yzvlekat znanyia yz massyvov ynformatsyy s pomoshchiu hlubokoi analytyky / Byll Franks; per. s anhl. Andreia Baranova. — M. : Mann, Yvanov y Ferber, 2014. — 352 p. (in Russian).

8 N.B. Shakhovska. Model Velykykh Danykh “Sutnist — kharakterystyka”. / N.B. Shakhovska, Yu.Ia. Boliubash / 2015 r. [Elektronnyi resurs] — Rezhym dostupu: D0%A2%D0%95%D0%A0%D0%98%D0%A1%D0%A2%D0%98%D0%9A%D0%90_ (in Ukrainian).

9 Cherniak Leonyd. Bolshye Dannye — novaia teoryia y praktyka. Otkrytye systemy. SUBD. — M.: Otkrytye systemy, 2011. — No 10. [Elektronnyi resurs] — Rezhym dostupu: (in Russian).

10 Uskenbaeva, R.K. Tasks of resources provision of distributed computer systems functionality / R.K. Uskenbayeva, A.A. Kuandykov, A.U. Kalizhanova. — Dubai, World Academy of Science, Engineering and Technology. — 2012. — Iss. 70. — P. 580–581.

11 R. Bekkerman, M. Bilenko, and J. Langford. Scaling up machine learning: Parallel and distributed approaches. 2011

12 H.B. McMahan. Follow-the-regularized-leader and mirror descent: Equivalence theorems and L1 regularization. In AISTATS, 2011.

13 H.B. McMahan and M. Streeter. Adaptive bound optimization for online convex optimization. In COLT, 2010.

14 Hrytsenko V.I. Zastosuvannia instrumentiv Big Data dlia pidvyshchennia efektyvnosti onlain reklamy. Ekonomiko-matematychne modeliuvannia sotsialno-ekonomichnykh system. Vypusk 21. — Kyiv, 2016. P 5–21 (in Ukrainian).

15 Big Data — Wikipedia. [Elektronnyi resurs] — Rezhym dostupu:

16 Chto takoe Real-Time Bidding. [Elektronnyi resurs] — rezhym dostupu: (in Russian).

17 Introduction to online machine learning: Simplified. [Elektronnyi resurs] — rezhym dostupu:

18 Riedman J. H. Regularization paths for generalized linear models via coordinate descent / Riedman J. H., Hastie T., Tibshirani R. / Journal of Statistical Software. 2010. Vol. 33, no. 1. pp. 1–22

19 L1- y L2-rehuliaryzatsyia v mashynnom obuchenyy. [Elektronnyi resurs] — rezhym dostupu: (in Russian).

20 L1-rehuliaryzatsyia lyneinoi rehressyy. Rehressyia naymenshykh uhlov (alhorytm LARS). [Elektronnyi resurs] — rezhym dostupu: chrome-extension: //ecnphlgnajanjnkcmbpancdjoidceilk/content/web/viewer.html?source=extension_pdfhandler & VetrovSem11_LARS.pdf (in Russian).

Received 28.09.2017

Issue 2 (188), article 6

Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.


May 23, 2017 the 80th anniversary of Vladimir Ilyich Gritsenko, known scientist in computer science, information technologies and its applications in economics, industrial and technological field, biological and medical cybernetics, computer technology training, director of the International Scientific and Training Center for Information Technologies and Systems. He is an initiator of development of a new class of high technologies — intelligent information technologies. Gritsenko V.I. is a member of a number of leading international and state councils of Ukraine on informatics, Permanent Representative of Ukraine to the Council of UNESCO Intergovernmental Programme on the information and communications, heads the UNESCO Chair “New Information Technologies in Education for All”, the chief editor of the scientific journals “Control Systems and Machines” and “Cybernetics”.

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Issue 2 (188), article 1


Kibern. vyčisl. teh., 2017, Issue 2 (188), pp.

Grytsenko V.I., Corresponding Member of NAS of Ukraine, Director
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of Ministry of Education and Science of Ukraine,
av. Acad. Glushkova, 40, Kiev, 03680, Ukraine

Rachkovskij D.A., Doctor of Engineering, Leading Researcher,
Dept. of Neural Information Processing Technologies,
International Research and Training Center for Information Technologies and Systems of the NAS of Ukraine and of Ministry of Education and Science of Ukraine,
av. Acad. Glushkova, 40, Kiev, 03680, Ukraine

Frolov A.A., Doctor of Biology, Professor,
Faculty of Electrical Engineering and Computer Science FEI,
Technical University of Ostrava, 17 listopadu 15, 708 33 Ostrava-Poruba, Czech Republic

Gayler R., PhD,
Independent Researcher,
Melbourne, VIC, Australia

Kleyko D., PhD post graduated,
Department of Computer Science, Electrical and Space Engineering,
Lulea University of Technology, 971 87 Lulea, Sweden

Osipov E., PhD, Professor,
Department of Computer Science, Electrical and Space Engineering,
Lulea University of Technology, 971 87 Lulea, Sweden


Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.

The purpose of the paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).

Scope. The survey is focused mainly on the networks of Hopfield, Willshaw, and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections, and networks with a bipartite graph structure for non-binary data with linear constraints.

Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.

Keywords: distributed associative memory, sparse binary vector, Hopfield network, Willshaw memory, Potts model, nearest neighbor, similarity search

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Recieved 15.04.2017

Issue 1 (187), article 1


Kibern. vyčisl. teh., 2017, Issue 1 (187), pp.5-11

Grytsenko V.I., Corresponding Member of NAS of Ukraine, Director of International
Research and Training Center for Information Technologies and Systems of National
Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine



May 5, 1997 the International Research and Training Center for Information Technologies and Systems NAS and MES of Ukraine was established by National Academy of Sciences of Ukraine.

During 20 years new scientific direction — Intelligent Information Technology (IIT), was formed. This methodology, the software and hardware became the basis for the deve-lopment of IIT of imaginative thinking, neural network technology, IIT for digital medicine, the E-education and intelligent control technologies.

The basic directions of fundamental and applied scientific research in the International Center are: creation of intelligent information technologies based on methods and means of imaginative thinking, comprehensive research of problems of intelligent management, intelligent robotics, digital medicine, e-learning, digital information space and technologies for the development of a secure information society.

By the main directions of the International Center, scientific schools in the field of information technologies and systems, technical cybernetics, biological and medical cybernetics, and mathematical analysis of comprehensive economic systems have been formed. An important contribution to the development of these scientific schools was made by outstanding Ukrainian scientists — academicians V.I. Skurikhin, A.G. Ivakhnenko,
N.M. Amosov and A.A. Bakaev. Their students and followers successfully develop these scientific directions in our country and abroad.

The International Center is the initiator of research and development of the concept of a new class of information technologies — intelligent information technologies. These are special, knowledge-intensive information technologies that differ from the known IT in the new quality — operating images of information objects. At the same time, an understanding of human speech, recognition of real and artificially created objects, active interaction with the environment, revealing the essence of the phenomenon, operating knowledge and the choice of strategy and tactics for achieving the set goal are achieved through the contours of intellectual IT.

Technical Committee for Standardization of information technologies, scientific journals “Control Systems and Computers” and “Cibernatics and Computer Engineering”, presentations of our scientists at prestigious international conferences, symposia and exhibitions make an important contribution for increasing the authority of the International Center.

The International Center has formed a program of work for the nearest years and defined the mechanisms for its implementation in the context of the rapid development of intellectualization of information technologies in all spheres of our society. As the comprehensive analysis showed, this program fully corresponds to global trends that the term “digital transformation” characterizes and covers the research priorities in information technology for a period of 5–10 years.

Keywords: intelligent information technology, imaginative thinking, intelligent management, digital medicine, e-learning, robotics, information society.

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