Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19
In this article, a cognitive map (CM) of COVID-19 morbidity in a given region was built. A general linear impulse process (IP) model in the CM was developed and measured, and unmeasured CM node coordinates were defined. The general IP model was decomposed into interrelated subsystems with measurable...
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System research and information technologies| _version_ | 1867334430421417984 |
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| author | Romanenko, Victor Miliavskyi, Yurii |
| author_facet | Romanenko, Victor Miliavskyi, Yurii |
| author_institution_txt_mv | [
{
"author": "Victor Romanenko",
"institution": "Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "Yurii Miliavskyi",
"institution": "Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
}
] |
| author_sort | Romanenko, Victor |
| baseUrl_str | http://journal.iasa.kpi.ua/oai |
| collection | OJS |
| datestamp_date | 2022-12-21T22:15:21Z |
| description | In this article, a cognitive map (CM) of COVID-19 morbidity in a given region was built. A general linear impulse process (IP) model in the CM was developed and measured, and unmeasured CM node coordinates were defined. The general IP model was decomposed into interrelated subsystems with measurable and unmeasurable node coordinates. For the subsystem with measurable node coordinates, multirate sampling of coordinates was conducted, resulting in the development of discrete dynamics models for quickly and slowly measured node coordinates. External controls were selected in IP models based on the possible variation of resources of node coordinates and CM weighting coefficients. IP control laws based on the variation of CM nodes and weight were designed. As a result, recurrent procedures for control generation in closed-loop control subsystems with multirate sampling were formulated. Experimental research on the control subsystems was carried out. It confirmed high efficiency for decreasing COVID-19 morbidity. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2022.3.03 |
| first_indexed | 2025-07-17T10:28:02Z |
| format | Article |
| fulltext |
V. Romanenko, Y. Miliavskyi, 2022
46 ISSN 1681–6048 System Research & Information Technologies, 2022, № 3
TIДC
ПРОБЛЕМИ ПРИЙНЯТТЯ РІШЕНЬ ТА
УПРАВЛІННЯ В ЕКОНОМІЧНИХ, ТЕХНІЧНИХ,
ЕКОЛОГІЧНИХ І СОЦІАЛЬНИХ СИСТЕМАХ
UDC 62.50
DOI: 10.20535/SRIT.2308-8893.2022.3.03
COMBINED CONTROL OF MULTIRATE IMPULSE PROCESSES
IN A COGNITIVE MAP OF COVID-19 MORBIDITY
V. ROMANENKO, Y. MILIAVSKYI
Abstract. In this article, a cognitive map (CM) of COVID-19 morbidity in a given
region was built. A general linear impulse process (IP) model in the CM was devel-
oped and measured, and unmeasured CM node coordinates were defined. The gen-
eral IP model was decomposed into interrelated subsystems with measurable and
unmeasurable node coordinates. For the subsystem with measurable node coordi-
nates, multirate sampling of coordinates was conducted, resulting in the develop-
ment of discrete dynamics models for quickly and slowly measured node coordi-
nates. External controls were selected in IP models based on the possible variation
of resources of node coordinates and CM weighting coefficients. IP control laws
based on the variation of CM nodes and weight were designed. As a result, recurrent
procedures for control generation in closed-loop control subsystems with multirate
sampling were formulated. Experimental research on the control subsystems was
carried out. It confirmed high efficiency for decreasing COVID-19 morbidity.
Keywords: cognitive map, impulse processes, control law, optimality criterion,
COVID-19.
INTRODUCTION
In the given article, cognitive modeling is applied to the research of dynamic
processes of coronavirus morbidity. Cognitive modeling is based on the notion of
a cognitive map (CM) which is defined as a weighted oriented graph with nodes
representing coordinates (concepts, factors, characteristics) of a complex system
and weighted edges (arcs) describing cause and effect interrelations between CM
nodes. CM is built by experts. It allows qualitative description of interrelations
between complex system’s components and quantitative assessment of the effect
of each CM node on all others, using edges of the oriented graph.
During evolution of a complex system with impulse-type behavior CM co-
ordinates evolve with time under the effect of different disturbances. Each coor-
dinate takes value )(kzi at discrete time moments ,...2,1,0k At the next sam-
pling period the value )1( kzi is determined by the value )(kzi and information
about increase or decrease of values of other nodes adjacent to given i -th node, at
time moment k . Change of any j -th node at time moment k is called “impulse”
and according to [1] is denoted by )(kPj and is given as a difference
Сombined control of multirate impulse processes in a cognitive map of COVID-19 morbidity
Системні дослідження та інформаційні технології, 2022, № 3 47
),1()()( kzkzkP jjj 0k . Impulse )(kPj incoming to the j -th node will
propagate over the paths of the CM to other nodes while increasing or decreasing.
Propagation process of disturbances in the CM is defined by the difference equation [1]
,,...,1),()()1(
1
nikPakzkz j
n
j
ijii
(1)
where ija is a weight of an oriented graph’s edge connecting the j -th node with
the i -th one. If an edge connecting i -th and j -th nodes is absent, respective co-
efficient 0ija .
CM nodes coordinates propagation rule (1) is often written as a first-order
difference euqatin in variables increments
),()1(
1
kzakz j
n
j
iji
(2)
which describes CM IP. Here .,...,2,1),1()()( nikzkzkz iii In a vector
form equation (2) is written as follows
)()1( kZAkZ , (3)
where A is a transposed adjacency matrix of the CM, )(kZ is a vector of in-
crements of coordinates iz of CM nodes, ni ,...,2,1 . From the control theory
perspective the model (3) describes dynamics of linear multivariate system in dis-
crete time under free motion of CM nodes.
Not all CM nodes are measurable in different complex systems. E.g., it is
impossible to accurately measure level of a population health, level of democracy
in a society, level of corruption and shadow economy, level of political activity
etc. To solve this problem, [2] suggests to decompose the initial CM model (2),
(3) into two interrelated CM. So, n CM nodes coordinates iz are broken down
into p measurable nodes ix ( ),...,1 pi and )( pn unmeasurable nodes ly ,
npl ,...,1 . Then IP model (3) can be presented as two interrelated subsystems
of IP:
)()()1( 1211 kYAkXAkX ; (4)
)()()1( 2221 kXAkYAkY , (5)
where X is a vector of measurable CM nodes, Y is a vector of unmeasurable
nodes. Here matrices of weights 12A , 22A represent interrelations between the
first (4) and the second (5) parts of the initial CM (3).
In [3] the problem of control automation for CM IP is solved by means of
varying CM nodes coordinates and weights with unirate sampling, where controls
are designed in a closed-loop control system. For this purpose equation (4) with
measurable coordinates is used, augmented by controls as follows:
),()()()()()1( 1211 kakLkuBkYAkXAkX
where )1()()( kukuku is the first difference of an external control vector
formed by means of varying resources of CM nodes coordinates,
V. Romanenko, Y. Miliavskyi
ISSN 1681–6048 System Research & Information Technologies, 2022, № 3 48
)1()()( kakaka is the first difference of a control vector based on vary-
ing the degree of influence )(kaij of the coordinate )(kx j on the coordinate
)(kxi . The rules for writing matrices B and )(kL are described in [3, 4]. Ap-
plying variations )(ku and )(ka as control inputs is necessary when dimen-
sions )(dim ku or )(dim ka are much less than number of nodes )(dim kX .
In such a case using only one group of controls significantly decreases accuracy
and speed of control systems for CM IP.
Among the coordinates of the vector X in the model (4) there can be some
coordinates fX measured (fixed) with a small sampling period 0T and some co-
ordinates sX measured with a big sampling period 0mTh where m is integer
greater than 1. To describe dynamics of such a system a model of an IP with mul-
tirate sampling was developed in [5].
CONSTRUCTION OF A COVID-19 MORBIDITY CM
Fig. 1 shows a CM of cause-and-effect relations in the process of spread of
COVID-19 morbidity in a given region. The following nodes are included into the
CM: 1 – number of daily revealed infected patients; 2 – number of daily vacci-
nated people; 3 – number of patients dying daily of COVID-19; 4 – number of
patients in isolation in the given region; 5 – number of patients recovered from
COVID-19 in the given region; 6 – number of infected passengers revealed dur-
ing arriving from other regions; 7 – number of patients in hospitals in the given
Fig. 1. COVID-19 morbidity CM
Сombined control of multirate impulse processes in a cognitive map of COVID-19 morbidity
Системні дослідження та інформаційні технології, 2022, № 3 49
region; 8 – degree of contacts intensity set for the population of the given region
when being in industrial, educational, public spaces; 9 – level of contact protec-
tion against infection (wearing masks); 10 – number of not isolated sick people
who move freely (including those who arrived from other regions and ere not re-
vealed at arrival); 11 – level of danger of the virus strain.
Each CM node affects other nodes. Degree of the effect is estimated by the
weights which can be positive or negative. E.g., the effect of the node 2 on the
node 10 is reflected by the coefficient -0.3 because increase of number of daily
vaccinated people leads to decrease of number of infected not isolated people who
freely move; increase of level of danger of the virus strain (node 11) leads to in-
crease of number of daily revealed infected patients with coefficient 0.4.
For cognitive modeling all CM nodes coordinates which represent factors of
different physical nature are usually kept in a single scale. That’s because when
building the weighted oriented graph experts cannot correctly set the weighting
coefficients of edges between CM nodes if they are measured in different units
(like level of wearing masks, number of patients, danger of the virus strain). Here
we suggest using 100 points scale for all CM nodes coordinates where 0 points
means absence of a given factor and 100 points means maximal possible level of
this factor at the given time interval. Obviously, when defining these factors
values some subjectivity is possible, but it does not interfere with modeling or
control of the whole system behavior.
MODELS DEVELOPMENT FOR CM IP SUBSYSTEMS WITH MULTIRATE
SAMPLING FOR QUICKLY AND SLOWLY MEASURED COORDINATES
We develop models with multirate coordinates sampling based on the model (3),
(5) of the subsystem with measurable coordinates. We assume that some coordi-
nates fX of the vector X belong to the quickly measured CM nodes with a
small sampling period 0T and some coordinates sX are measured in discrete time
moments with a big sampling period 0mTh . Then the IP model (5) can be
generally written in an intermediate form with unirate sampling as follows:
)(
)(
0
0
)(
)(
)1(
)1(
1111
1111
ku
ku
B
B
kX
kX
AA
AA
kX
kX
s
f
s
f
s
f
ssf
fsf
s
f
).(
)(
)(
)(0
0)(
12
12 kY
A
A
ka
ka
kL
kL
s
f
s
f
s
f
(6)
Quickly measured coordinates fX are nodes 1, 2, 3, 4, 5, 6, 7 of the CM
(Fig. 1). Slowly measured node sx is the CM node 8 – degree of contacts inten-
sity for the population of the given region. Unmeasured coordinates are nodes 9,
10, 11. To create controls )(2 ku , )(4 ku , )(8 ku we can use varying the re-
sources of the nodes 2, 4, 8 respectively. Varying the weight coefficient 67a (how
number of infected passengers revealed during arriving from other regions affects
number of patients in hospitals) can be used to create a control )(67 ka . Then
V. Romanenko, Y. Miliavskyi
ISSN 1681–6048 System Research & Information Technologies, 2022, № 3 50
sizes of the vectors in the model (6) will be ,7dim fX ,1dim sX
,2dim fu ,1dim su ,3dim Y ,1dim fa 0dim sa .
According to CM on Fig. 1, matrices in the model (6) will be the following:
,
2.025.000025.01.0
0000000
6.0002.05.04.00
07.00002.04.0
0015.00005.00
00001.003.0
000002.03.0
11
fA
,25.00002.006.0 T
11 fsA
,05.00005.0002.011 sfA .011 sA
Matrix fB is created by the CM IP control system designer. It has to ensure
scaling and switching designed controls )(ku f . Elements of the matrix fB are
zeros and ones. Element 1ib when the i -th CM node is affected by the -th
component of the control vector. Thus in each row of the matrix fB only one el-
ement can be equal to one and all others will be zero. Size of the matrix fB for
the given CM (6) is 27 where 7 and 2 are sizes of vectors fX and )(ku f re-
spectively. Then
T
0001000
0000010
fB .
The rules for writing the matrix )(kL f can be found in [3]. For the model
(6) T6 0)(00000)( kxkL f .
Unmeasurable CM nodes coordinates 11109 ,, yyy in the model (6) are in-
cluded into the vector )(kY as unmeasurable disturbances. Then matrix fA12 in
the CM (Fig. 1) will be
.
02.00
6.000
04.00
03.00
3.000
2.000
4.06.05.0
12
fA
Сombined control of multirate impulse processes in a cognitive map of COVID-19 morbidity
Системні дослідження та інформаційні технології, 2022, № 3 51
There is only one slowly measurable coordinate 8 in the CM which can be
affected by the control su with a sampling period 0mTh via varying resources
of the node 8. So in model (6) 0)(,1 kLB ss . Unmeasurable nodes 9, 10, 11
don’t affect the node 8, so 012 sA .
Thus IP model (5) is split into two parts. The first part describing dynamics
of the quickly measured CM nodes with sampling period 0T can be written based
on (6) as follows:
h
m
k
xAlTh
m
k
XATlh
m
k
X sfsfff
~)1( 110110
,0
0
0
0
lTh
m
k
lTh
m
k
a
lTh
m
k
u
lTh
m
k
LB f
f
f
ff (7)
where
m
k
is integer part of dividing k by m , 1,...,1,0 ml . The first differ-
ence
00 lTh
m
k
XlTh
m
k
X ff
0)1( Tlh
m
k
X f ,
h
m
k
xh
m
k
xh
m
k
x sss 1~ if 0l
and zero otherwise. Disturbances vector
0120 lTh
m
k
YAlTh
m
k
ff
is generated by the unmeasurable nodes Y of the CM.
The second part of the model described the dynamics of the slowly measured
CM node 8 can be written in the intermediate form based on (6) as
.)1( 00110
lTh
m
k
uBlTh
m
k
XATlh
m
k
x ssfsfs (8)
Based on the iterative procedure described in [5] the model (8) can be transi-
tioned to the form where the coordinate sx and the control su have the big sam-
pling period 0mTh , considering that 011 sA and there are no external distur-
bances and weights-varying based controls:
,)1(
~
1 011
h
m
k
uBTmh
m
k
XAh
m
k
x ssfsfs (9)
where ;11
h
m
k
xh
m
k
xh
m
k
x sss
V. Romanenko, Y. Miliavskyi
ISSN 1681–6048 System Research & Information Technologies, 2022, № 3 52
000 1111
~
Tmh
m
k
XTmh
m
k
XTmh
m
k
X fff .
CONTROL AUTOMATION OF CM IP WITH MULTIRATE SAMPLING
For designing algorithms of CM IP automated control dynamics (7), (9) of the
vectors fX , sx should be written in full CM nodes coordinates (not in incre-
ments):
0)1( Tlh
m
k
X f
h
m
k
xAlTh
m
k
XqAAI sfsffff
~)( 110
1
111111
;0
0
0
0
lTh
m
k
lTh
m
k
a
lTh
m
k
u
lTh
m
k
LB f
f
f
ff (10)
h
m
k
xs 1
,)1(
~
011
h
m
k
uBTmh
m
k
XAh
m
k
x ssfsfs (11)
where 1q is a reverse shift operator with sampling period 0T .
To design quickly changes controls ff au , the following quadratic crite-
rion is suggested:
T
00 )1()1( fff GTlh
m
k
XETlh
m
k
J
00
T
0)1( lTh
m
k
alTh
m
k
uGTlh
m
k
X ffff
0
0
2
1
0
0
lTh
m
k
a
lTh
m
k
u
r
R
f
f
f
f
, (12)
where E is expectation (mean), fG is set-point vector for stabilization of CM
nodes coordinates fX .
Сombined control of multirate impulse processes in a cognitive map of COVID-19 morbidity
Системні дослідження та інформаційні технології, 2022, № 3 53
Based on minimization of criterion (12) with respect to vector
f
f
a
u
, having
used model (10), we find quick combined control vector that affects nodes fX
according to (10):
0
0
lTh
m
k
a
lTh
m
k
u
f
f
0
T
T1
2
1
0
0
T
T
0
0
lTh
m
k
L
B
r
R
lTh
m
k
LB
lTh
m
k
L
B
f
f
f
f
ff
f
f
h
m
k
xAlTh
m
k
XqAAI sfsffff
~)( 110
1
111111
ff GlTh
m
k
0 . (13)
To design the slow control su , the second optimality criterion is suggested:
h
m
k
urGh
m
k
xEh
m
k
J sssss
2
2
11 , (14)
where sG is set-point vector for sx stabilization. Based on minimization of crite-
rion (14) with respect to su , having used model (11), we find slow combined
control that affects node 8:
.)1(
~
0112
sfsfs
ss
s
s GTmh
m
k
XAh
m
k
x
rB
B
h
m
k
u (15)
EXPERIMENTAL RESEARCH OF THE IP CONTROL SYSTEM IN THE
COVID-19 MORBIDITY CM
For a computational simulation initial values of CM nodes coordinates were set at
the medium levels 11,...,1,50 ixi . Problem statement of the experiments is to
move CM nodes coordinates 8742 ,,, xxxx to the new levels ,602 G ,404 G
40,40 87 GG . It means that we need to increase number of daily vaccinated
people ( 2x ), decrease number of patients in isolation in the given region ( 4x ),
decrease of patients in hospitals in the given region ( 7x ), decrease degree of con-
tacts intensity set for the population of the given region when being in industrial,
V. Romanenko, Y. Miliavskyi
ISSN 1681–6048 System Research & Information Technologies, 2022, № 3 54
educational, public spaces ( 8x ). In the fast control subsystem (13) control vector
consists of the controls T42 uuu f and 67aa f , and in the slow
subsystem (15) there is only one control )()( 8 rhurhus , where
m
k
r . Ra-
tio between sampling periods of fast and slow subsystems 0mTh is selected
with the coefficient 6m .
Fig. 2 shows the charts for the results of simulation of CM nodes
11,...,1, ixi , and Fig. 3 demonstrates charts of the generated increments of con-
trols )(),(),(),( 80670402 rhukTakTukTu based on control laws (13) and (15).
Based on the charts analysis we can formulate the following tendencies in
the changes of CM nodes coordinates and controls with multirate sampling.
1. CM nodes coordinates 8742 ,,, xxxx which are directly controlled by
)(),(),(),( 80670402 rhukTakTukTu respectively quickly shift to their new
levels 40,40,40,60 8742 GGGG .
2. Nodes coordinates 6531 ,,, xxxx which are not controlled directly move
slower to the new natural level, i.e. 631 ,, xxx decrease and 5x increases.
3. Controls in the form of increments ,)( 02 kTu ,)( 04 kTu ),( 067 kTa
)(8 rhu set at zero levels when transient processes are over.
10 20 30 40
10
20
30
40
50
x
1
(kT
0
)
10 20 30 40
50
52
54
56
58
x
2
(kT
0
)
10 20 30 40
46
47
48
49
50
x
3
(kT
0
)
10 20 30 40
40
45
50
x
4
(kT
0
)
10 20 30 40
50
55
60
x
5
(kT
0
)
10 20 30 40
48.5
49
49.5
50
x
6
(kT
0
)
10 20 30 40
35
40
45
50
x
7
(kT
0
)
10 20 30 40
42
44
46
48
50
x
8
(rh)
10 20 30 40
44
46
48
50
x
9
(kT
0
)
10 20 30 40
20
30
40
50
x
10
(kT
0
)
10 20 30 40
47.5
48
48.5
49
49.5
50
x
11
(kT
0
)
Fig. 2. Nodes coordinates changes
Сombined control of multirate impulse processes in a cognitive map of COVID-19 morbidity
Системні дослідження та інформаційні технології, 2022, № 3 55
CONCLUSIONS
This article develops a CM which quantitatively describes interrelations between
factors during the spread of COVID-19 morbidity. Based on the CM the IP models
are developed which describe dynamic aspects of the morbidity in the form of
difference equations. Both measurable and unmeasurable coordinates are ac-
counted for. We also account for presence of both quickly and slowly measured
coordinates, which are reflected in the subsystems models with fast and slow
sampling. For these subsystems external controls are selected, which are then
generated based on the linear quadratic control method using combined resources
varying of some nodes coordinates and edges weights in the CM.
Experimental research was conducted by means of computational simulation
of the CM IP closed-loop control system. Based on the charts of the transients
processes of CM nodes coordinates and incremental controls it is concluded that
directly controlled CM nodes quickly shift to the new levels defined by the set-
points of the controller. Not directly controlled CM nodes coordinates move to the
new levels slower.
REFERENCES
1. F. Roberts, Discrete Mathematical Models with Applications to Social, Biological,
and Environmental Problems. Englewood Cliffs, Prentice-Hall, 1976, 559 p.
2. Mikhail Z. Zgurowsky, Victor D. Romanenko, and Yuriy L. Milyavskiy, “Principles
and Methods of Impulse Processes Control in Cognitive Maps of Complex Systems.
Fig. 3. Controls changes
V. Romanenko, Y. Miliavskyi
ISSN 1681–6048 System Research & Information Technologies, 2022, № 3 56
Part 1,” Journal of Automation and Information Sciences, vol. 48, no. 3, pp. 36–45,
2016. doi: 10.1615/JAutomatInfScien.v48.i3.40.
3. V. Romanenko and Y. Milyavsky, “Control automation method in cognitive maps
based on the synthesis of increments of weighting coefficients and nodes coordi-
nates,” (in rus.), System Research and Information Technologies, no. 3, pp. 89–99,
2019. doi: 10.20535/SRIT.2308-8893.2019.3.08.
4. V. Romanenko and Y. Milyavsky, “Control method in cognitive maps based on
weights increments,”Cybernetics and Computer Engineering, issue 184, pp. 44–55,
2016. doi: 10.15407/kvt184.02.044.
5. V. Romanenko, Y. Miliavskyi, and H. Kantsedal, “Application of Impulse Process
Models with Multirate Sampling in Cognitive Maps of Cryptocurrency for Dynamic
Decision Making,” in System Analysis & Intelligent Computing. Theory and
Applications; eds: M. Zgurovsky, N. Pankratova. Springer, 2022, pp. 115–137. doi:
10.1007/978-3-030-94910-5.
Received 10.08.2022
INFORMATION ON THE ARTICLE
Victor D. Romanenko, ORCID: 0000-0002-6222-3336, Educational and Research Insti-
tute for Applied System Analysis of the National Technical University of Ukraine “Igor
Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: romanenko.viktorroman@gmail.com
Yurii L. Miliavskyi, ORCID: 0000-0003-0882-3418, Educational and Research Institute
for Applied System Analysis of the National Technical University of Ukraine “Igor Sikor-
sky Kyiv Polytechnic Institute”, Ukraine, e-mail: yuriy.milyavsky@gmail.com
КОМБІНОВАНЕ КЕРУВАННЯ ІМПУЛЬСНИМИ ПРОЦЕСАМИ З
РІЗНОТЕМПОВОЮ ДИСКРЕТИЗАЦІЄЮ В КОГНІТИВНІЙ КАРТІ
ЗАХВОРЮВАНОСТІ НА COVID-19 / В.Д. Романенко, Ю.Л. Мілявський
Анотація. Побудовано когнітивну карту (КК) розповсюдження захворюванос-
ті на COVID-19 в даному регіоні. Розроблено загальну лінійну модель імпуль-
сних процесів (ІП) КК і проведено аналіз вимірюваних і невимірюваних коор-
динат вершин КК. Виконано декомпозицію загальної моделі ІП на
взаємопов’язані підсистеми з вимірюваними і не вимірюваними координатами
вершин. Для підсистеми з вимірюваними координатами вершин проведено рі-
знотемпову дискретизацію координат, у результаті чого розроблено дискретні
моделі динаміки для швидковимірюваних і повільновимірюваних координат
вершин КК. Вибрано зовнішні керувальні дії в моделях ІП з урахуванням мож-
ливого варіювання ресурсами координат вершин і вагових коефіцієнтів ребер
КК. Виконано синтез законів керування ІП на основі варіювання координат
вершин і вагового коефіцієнта. Розроблено рекурентні процедури формування
керувальних дій у замкнених підсистемах керування з різнотемповою дискре-
тизацією. Проведено експериментальні дослідження підсистем керування, які
підтверджують високу ефективність по зниженню захворюваності на COVID-19.
Ключові слова: когнітивна карта, імпульсні процеси, закони керування, кри-
терій оптимальності, COVID-19.
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| id | journaliasakpiua-article-269395 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-07-17T10:28:02Z |
| publishDate | 2022 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/1a/de73f6a7464b045566c56f8e58ef811a.pdf |
| spelling | journaliasakpiua-article-2693952022-12-21T22:15:21Z Combined control of multirate impulse processes in a cognitive map of COVID-19 morbidity Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 Romanenko, Victor Miliavskyi, Yurii когнітивна карта імпульсні процеси закони керування критерій оптимальності COVID-19 cognitive map impulse processes control law optimality criterion COVID-19 In this article, a cognitive map (CM) of COVID-19 morbidity in a given region was built. A general linear impulse process (IP) model in the CM was developed and measured, and unmeasured CM node coordinates were defined. The general IP model was decomposed into interrelated subsystems with measurable and unmeasurable node coordinates. For the subsystem with measurable node coordinates, multirate sampling of coordinates was conducted, resulting in the development of discrete dynamics models for quickly and slowly measured node coordinates. External controls were selected in IP models based on the possible variation of resources of node coordinates and CM weighting coefficients. IP control laws based on the variation of CM nodes and weight were designed. As a result, recurrent procedures for control generation in closed-loop control subsystems with multirate sampling were formulated. Experimental research on the control subsystems was carried out. It confirmed high efficiency for decreasing COVID-19 morbidity. Побудовано когнітивну карту (КК) розповсюдження захворюваності на COVID-19 в даному регіоні. Розроблено загальну лінійну модель імпульсних процесів (ІП) КК і проведено аналіз вимірюваних і невимірюваних координат вершин КК. Виконано декомпозицію загальної моделі ІП на взаємопов’язані підсистеми з вимірюваними і не вимірюваними координатами вершин. Для підсистеми з вимірюваними координатами вершин проведено різнотемпову дискретизацію координат, у результаті чого розроблено дискретні моделі динаміки для швидковимірюваних і повільновимірюваних координат вершин КК. Вибрано зовнішні керувальні дії в моделях ІП з урахуванням можливого варіювання ресурсами координат вершин і вагових коефіцієнтів ребер КК. Виконано синтез законів керування ІП на основі варіювання координат вершин і вагового коефіцієнта. Розроблено рекурентні процедури формування керувальних дій у замкнених підсистемах керування з різнотемповою дискретизацією. Проведено експериментальні дослідження підсистем керування, які підтверджують високу ефективність по зниженню захворюваності на COVID-19. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2022-10-30 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/269395 10.20535/SRIT.2308-8893.2022.3.03 System research and information technologies; No. 3 (2022); 46-56 Системные исследования и информационные технологии; № 3 (2022); 46-56 Системні дослідження та інформаційні технології; № 3 (2022); 46-56 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/269395/264885 |
| spellingShingle | когнітивна карта імпульсні процеси закони керування критерій оптимальності COVID-19 Romanenko, Victor Miliavskyi, Yurii Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 |
| title | Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 |
| title_alt | Combined control of multirate impulse processes in a cognitive map of COVID-19 morbidity |
| title_full | Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 |
| title_fullStr | Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 |
| title_full_unstemmed | Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 |
| title_short | Комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на COVID-19 |
| title_sort | комбіноване керування імпульсними процесами з різнотемповою дискретизацією в когнітивній карті захворюваності на covid-19 |
| topic | когнітивна карта імпульсні процеси закони керування критерій оптимальності COVID-19 |
| topic_facet | когнітивна карта імпульсні процеси закони керування критерій оптимальності COVID-19 cognitive map impulse processes control law optimality criterion COVID-19 |
| url | https://journal.iasa.kpi.ua/article/view/269395 |
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