Open AccessModified simplified algorithm of the method of group consideration of arguments in simulation processes
Author(s) -
Nataliia Ravska,
Eugene Korbut,
Oleksiy Ivanovskyi,
Radion Rodin,
Valeria Parnenko,
Oleksandr Zakovorotnyi,
Oleksandr Klochko,
Serhiy Sapon,
Rolahd Loroch
Publication year - 2021
Publication title -
progresivna tehnìka, tehnologìâ ta ìnženerna osvìta
Language(s) - English
Resource type - Journals
ISSN - 2409-7160
DOI - 10.20535/2409-7160.2021.xxii.240466
Subject(s) - orthogonalization , heuristic , artificial neural network , process (computing) , computer science , group (periodic table) , algorithm , variable (mathematics) , class (philosophy) , collinearity , argumentation theory , artificial intelligence , mathematics , mathematical analysis , philosophy , chemistry , geometry , organic chemistry , epistemology , operating system
There are many types and methods of simulation, but among them special attention should be paid to methods based on the theory of heuristic self-organization. All algorithms of the method of group argumentation (MGVA) are characterized by structural commonality on the principle of self-organization, which require insignificant requirements for a priori information to search for an infinite number of options. The advantage of the algorithm of the method of group
consideration of arguments in comparison with other algorithms of this class is the presence of possibilities of expansion of the vector of initial data and the device for elimination of collinearity - reception of orthogonalization. MGVA consists of two blocks: pre-processing of observations taking into account the system of selected reference functions and calculation of selection applicants. As a result of the algorithm, models capable of controlling the process taking into account the phenomena accompanying a certain process are obtained. Given the commonality of the main provisions of the theory of self-organization of artificial neural networks and MGVA, the network variables are added to the model as a variable Z. As a result, we obtain a neural network that describes the physical phenomena accompanying the process. This will significantly increase the efficiency and accuracy of process management.