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APPLICATION OF NEURAL NETWORK TECHNOLOGIES FOR MANAGEMENT DEVELOPMENT OF SYSTEMS

https://doi.org/10.17747/2618-947X-923

Abstract

Work is devoted to application of neural network technologies for management development of systems. In article the analysis of efficiency of introduction of neural network technologies is carried out to business processes of three Russian companies and the positive effect locates when using neural networks in several parameters.

The case analysis is added with the analysis of economic feasibility of introduction of neural networks by means of an assessment of studied indicators, an assessment of satisfaction of clients, control of the personnel, an assessment of efficiency of each employee. Recommendations about application of neural networks in the organization are made.

In article it is shown that in spite of the fact that many actions necessary for introduction of system, are costly and long-term, they will positively affect company activity.

About the Author

A. L. Lisovsky
“Krypten” JSC
Russian Federation

Candidate of economic sciences, general director of “NPO “Krypten” JSC. Research interests: formation of the development strategy of industrial companies, change management, transformation of industrial production.



References

1. Akhmetzyanov K.R., Tur A.I., Kokoulin A.N., Yuzhakov A.A. (2020). Optimizatsiya vychisleniy neyronnoy seti [Optimization of neural network computation]. Vestnik PNIPU. Elektrotekhnika, informatsionnye tekhnologii, sistemy upravleniya [PNRPU Bulletin. Electrotechnics, Informational Technologies, Control Systems], 36.

2. Wiener N. (1968). Kibernetika, ili Upravlenie i svyaz’ v zhivotnom i mashine [Cybernetics: Or control and communication in the animal and the machine]. Trans. from Eng. Мoscow, Sovetskoe radio.

3. Zuev V.N., Kemaykin V.K. (2019). Modifitsirovannyy algoritm obucheniya neyronnykh setey [An improved neural network training algorithm]. Programmnye produkty i sistemy [Software & Systems], 32(2), 258-262. DOI: 10.15827/0236-235X.126.258-262.

4. Kovalev D.A. (2020). Glubokie neyronnye seti. Primenenie v meditsine [Deep neural networks. Medical applications]. Simvol nauki [Symbol of Science], 4, 29-31.

5. Kornina A. (2018). Mashinnoe obuchenie i neyronnye seti v biznese [Machine learning and neural networks in business]. Khronoekonomika [HronoEconomics], 2(10), 110-115.

6. Kurnikov D.S., Petrov S.A. (2017). Ispol’zovanie neyronnykh setey v ekonomike [The use of neural networks in the economy]. Juvenis Scientia, 6, 10-12.

7. Linder N.V., Arsenova E.V. (2016). Instrumenty stimulirovaniya innovatsionnoy aktivnosti kholdingov v promyshlennosti [Instruments of stimulation of innovative activity of holdings in the industry]. Nauchnye trudy Vol’nogo ekonomicheskogo obshchestva Rossii [Scientific Works of VEO of Russia], 198(2), 266-274.

8. Minsky M., Papert S. (1971). Perseptrony [Perceptrons]. Trans. from Eng. Мoscow, Mir.

9. Morkhat P.M. (2018). Iskusstvennyy intellekt: nekotorye itogi obrabotki rezul’tatov provedeniya ekspertnykh oprosov spetsialistov [Artificial intelligence: Some results of processing the results of expert surveys of specialists]. Nravstvennye imperativy v prave [Moral Imperatives in Law], 2.

10. Naumova M.Ya., Sharafutdinov A.G. (2015). Iskusstvennyy intellekt - budushchee segodnya [Artificial Intelligence - the future today]. NovaInfo.Ru, 34(2), 67-69.

11. Trachuk A.V., Linder N.V., Tarasov I.V., Nalbandyan G.G., Khovalova T.V., Kondratyuk T.V., Popov N.A. (2018). Transformatsiya promyshlennosti v usloviyakh chetvertoy promyshlennoy revolyutsii [Transformation of industry in the context of the Fourth Industrial Revolution]. Moscow, Financial University under the Government of the Russian Federation.

12. Tsvenger I.G., Nizamov I.R. (2017). Primenenie neyrosetevykh regulyatorov v sistemakh upravleniya elektroprivodami [Applying neural network regulators in system control of electric drives]. Vestnik Kazanskogo tekhnologicheskogo universiteta [Bulletin of the Kazan Technological University], 20(8), 111-114.

13. Yunusova L.R., Magsumova A.R. (2019). Algoritmy obucheniya iskusstvennykh neyronnykh setey [Algorithms for training artificial neural networks]. Problemy nauki [Science Problems], 7(43), 21-25.

14. Aleksander I., Morton H. (1990). An introduction to neural computing. London, Chapman & Hall.

15. Ashby W.R. (1952). Design for a brain. New York, Wiley.

16. Bashkirov O.A., Bravermann E.M., Muchnik I.B. (1964). Potential function algorithms for pattern recognition learning machines. Automation and Remote Control, 25, 629-631.

17. Beitz C.R. (2018). The idea of human rights. New York, Oxford University Press.

18. Bowman D.M., Garden H., Stroud C., Winickoff D.E. (2018). The neurotechnology and society interface: Responsible innovation in an international context. Journal of Responsible Innovation, 5(1), 1-12.

19. Broomhead D.S., Lowe D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-355.

20. Cowan J.D. (1967). A mathematical theory of central nervous activity. Ph.D. Thesis. London, University of London.

21. Hebb D.O. (1949). The organization of behavior: A neuropsychological theory. New York, Wiley.

22. Kohonen T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 59-69.

23. McCulloch W.S., Pitts W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133.

24. Minsky M.L. (1954). Theory of neural-analog reinforcement systems and its application to the brain-model problem. Ph.D. Thesis. Princeton, NJ., Princeton University.

25. Minsky M.L. (1961). Steps toward artificial intelligence. Proceedings of the Institute of Radio Engineers, 49, 8-30.

26. Ramon y Cajal S. (1911). Histologie du systeme nerveux de l’homme et des vertebres. Paris, Maloine.

27. Rochester N., Holland J.H., Haibt L.H., Duda W.L. (1956). Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Transactions on Information Theory, IT-2, 80-93.

28. Rosellini W., D’Haese P.-F. (2017). Data is driving the future of neurotechnology with cranialcloud. ONdrugDelivery, 81, 44-47.

29. Rosenblatt F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-408.

30. Rumelhart D., Hinton G., Williams R. (1986). Learning representations by back-propagating errors. Nature (London), 323, 533-536.

31. Uttley A.M. (1956). A theory of the mechanism of learning based on conditional probabilities. Proc. of the 1st International Conference on Cybernetics, Namur, Gauthier-Villars, Paris, 83-92.

32. Uttley A.M. (1979). Information transmission in the nervous system. London, Academic Press.

33. Widrow B. (1962). Generalisation and information storage in networks of adaline “neurons”. In: Yovitz M.C., Jacobi G.T., Goldstein G.D. (eds.). Self-Organizing Systems. Washington, DC, Sparta.

34. Willshaw D.J., Malsburg C. von der (1976). How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London, Series B, 194, 431-445.

35. Winograd S., Cowan J.D. (1963). Reliable computation in the presence of noise. Cambridge, MA, MIT Press.


Review

For citations:


Lisovsky A.L. APPLICATION OF NEURAL NETWORK TECHNOLOGIES FOR MANAGEMENT DEVELOPMENT OF SYSTEMS. Strategic decisions and risk management. 2020;11(4):378-389. https://doi.org/10.17747/2618-947X-923

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