Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Invariance preserving control of clusters recognized in networks of kuramoto oscillators. / Granichin, Oleg; Uzhva, Denis.
Artificial Intelligence : 18th Russian Conference, RCAI 2020, Proceedings. ред. / Sergei O. Kuznetsov; Aleksandr I. Panov; Konstantin S. Yakovlev. Springer Nature, 2020. стр. 472-486 (Lecture Notes in Computer Science ; Том 12412 LNAI).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Invariance preserving control of clusters recognized in networks of kuramoto oscillators
AU - Granichin, Oleg
AU - Uzhva, Denis
N1 - Granichin O., Uzhva D. (2020) Invariance Preserving Control of Clusters Recognized in Networks of Kuramoto Oscillators. In: Kuznetsov S.O., Panov A.I., Yakovlev K.S. (eds) Artificial Intelligence. RCAI 2020. Lecture Notes in Computer Science, vol 12412. Springer, Cham. https://doi.org/10.1007/978-3-030-59535-7_35
PY - 2020
Y1 - 2020
N2 - The Kuramoto model is able to describe a huge variety of examples of synchronization in the real world. We re-consider it through the framework of the network science and study the phenomenon of a particular interest, agent clustering. We assume that clusters are already recognized by some algorithm and then consider them as new variables on mesoscopic scale, which allows one to significantly reduce the dimensionality of a complicated (complex) system, thus reducing the required number of control inputs. In contrast to the common approach, where each agent is treated separately, we propose an alternative one using a supplementary control input, which is equal for the whole cluster. We also perform an analysis of this input by finding its limitations required for cluster structure to remain invariant in a network of Kuramoto oscillators. The theoretical results are demonstrated on a simulated multi-agent network with multiple clusters.
AB - The Kuramoto model is able to describe a huge variety of examples of synchronization in the real world. We re-consider it through the framework of the network science and study the phenomenon of a particular interest, agent clustering. We assume that clusters are already recognized by some algorithm and then consider them as new variables on mesoscopic scale, which allows one to significantly reduce the dimensionality of a complicated (complex) system, thus reducing the required number of control inputs. In contrast to the common approach, where each agent is treated separately, we propose an alternative one using a supplementary control input, which is equal for the whole cluster. We also perform an analysis of this input by finding its limitations required for cluster structure to remain invariant in a network of Kuramoto oscillators. The theoretical results are demonstrated on a simulated multi-agent network with multiple clusters.
KW - Agents-based systems
KW - Control of networks
KW - Nonlinear output feedback
UR - http://www.scopus.com/inward/record.url?scp=85092148582&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/f273d5e1-fb19-38db-aec8-90ccb883086c/
U2 - 10.1007/978-3-030-59535-7_35
DO - 10.1007/978-3-030-59535-7_35
M3 - Conference contribution
AN - SCOPUS:85092148582
SN - 9783030595340
T3 - Lecture Notes in Computer Science
SP - 472
EP - 486
BT - Artificial Intelligence
A2 - Kuznetsov, Sergei O.
A2 - Panov, Aleksandr I.
A2 - Yakovlev, Konstantin S.
PB - Springer Nature
T2 - 18th Russian Conference on Artificial Intelligence, RCAI 2020
Y2 - 10 October 2020 through 16 October 2020
ER -
ID: 69965314