Open AccessSynergetic control algorithms for a multidimensional biomedical model under conditions of nonrandom and random disturbances using kernel regression
Author(s) -
С. И. Колесникова,
V. A. Avramyonok
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1745/1/012094
Subject(s) - kernel (algebra) , object (grammar) , control variable , nonlinear system , mathematics , computer science , variable (mathematics) , random variable , function (biology) , algorithm , control theory (sociology) , control (management) , mathematical optimization , artificial intelligence , statistics , discrete mathematics , mathematical analysis , physics , quantum mechanics , evolutionary biology , biology
The purpose is to investigate the applicability of the principles of the synergetic control theory to a primitive object with the initial description in the form of a system of nonlinear differential equations with a delay. In terms of intensionality, control will be here understood as the rule of intake of the substance (agent) into a living organism, which serve as antidotes to a disease, in the form of a pair of variables (time of the substance administration and amount of the administered substance). Based on a new algorithm of a discrete nonlinear analytical synthesis, three systems of control over such an object are designed for the following cases: 1) a control object is functioning under completely determined conditions; 2) a control object is affected by the deterministic unknown constrained disturbances via the control variable; 3) presence of an additive random noise on any variable. The results of a comparative numerical modeling of the designed control systems are presented, which function without any preliminary filtration of the random variable measurements and with a filtration by a kernel regression algorithm. The results can be used in the decision support systems.