Abstract
The work is devoted to the concept of decision synthesis within the components as a part of technical systems of intelligent data processing. A methodology of experimental research aimed at substantiating the scientific and practical significance of this concept has been developed and implemented. As an object of research, we used a computer model of the component of decision-making, which provides a pattern recognition procedure based on data obtained about an external analyzed object. The results of the research represent the response of the object to changes in external conditions that affect the formation of the decision. It was revealed that the incorporation of the principles of decision synthesis into the object of research promoted the emergence of cognitive mechanisms in the process of information processing, which led to an increase in the adaptive abilities of the technical system to change the external conditions of its existence.
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References
Danilin, S.N., Makarov, M.V. Shchanikov, S.A.: Design of artificial neural networks with a specified quality of functioning. In: IEEE International Conference Engineering and Telecommunication, pp. 67–71 Russia (2014)
Sani, A.: Machine Learning for Decision Making. Université de Lille 1 (2015)
Kashyap, P.: Machine Learning for Decision Makers. Apress, Berkeley (2017)
Bishop, C.: Pattern Recognition and Machine Learning, p. 738. MIT Press, Cambridge (2018)
Chandiok, A., Chaturvedi, D.K.: Machine learning techniques for cognitive decision making. In: IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI) (2015). https://doi.org/10.1109/wci.2015.7495529
Lu, H.: Artificial Intelligence and Robotics, p. 326. Springer, Berlin (2018)
Meyer, G., Adomavicius, G., et al.: A machine learning approach to improving dynamic decision making. Inf. Syst. Res. 25(2), 239–263 (2014)
Minsky, M.: Computation: Finite and Infinite Machines. Prentice-Hall Inc., Hoboken (1967)
Makarov, M.V.: Practical analysis of the properties of nanoscale electronic elements aimed at their application when designing parallel architecture computing systems. J. Nano Electron. Phys. 3(8), 03023-1−03023-4 (2016). https://doi.org/10.21272/jnep.8(3).03023
Makarov, M.: Investigating the physical and information parameters of nanoscale electronic elements as part of the computing systems with the neural network architecture. Mater. Phys. Mech. 3(41), 78–83 (2019). https://doi.org/10.18720/MPM.4112019_12
Acknowledgments
The reported study was funded by RFBR, project number 20-07-00951.
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Makarov, M., Astafiev, A. (2022). Researching the Fundamentals of Decision Synthesis into Technical Systems of Intelligent Data Processing. In: Radionov, A.A., Gasiyarov, V.R. (eds) Advances in Automation III. RusAutoCon 2021. Lecture Notes in Electrical Engineering, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-94202-1_39
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DOI: https://doi.org/10.1007/978-3-030-94202-1_39
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