Перегляд за Автор "Lyubchyk, L. M."

Зараз показуємо 1 - 2 з 2
  • Ескіз
    ДокументВідкритий доступ
    Comparative analysis of modified semi-supervised learning algorithms on a small amount of labeled data
    (КПІ ім. Ігоря Сікорського, 2022) Lyubchyk, L. M.; Yamkovyi, K. S.
    The paper is devoted to improving semi-supervised clustering methods and comparing their accuracy and robustness. The proposed approach is based on expanding a clustering algorithm for using an available set of labels by replacing the distance function. Using the distance function considers not only spatial data but also available labels. Moreover, the proposed distance function could be adopted for working with ordinal variables as labels. An extended approach is also considered, based on a combination of unsupervised k-medoids methods, modified for using only labeled data during the medoids calculation step, supervised method of k nearest neighbor, and unsupervised k-means. The learning algorithm uses information about the nearest points and classes’ centers of mass. The results demonstrate that even a small amount of labeled data allows us to use semi-supervised learning, and proposed modifications improve accuracy and algorithm performance, which was found during experiments.
  • Ескіз
    ДокументВідкритий доступ
    Decentralized leader-following con-sensus control design for discrete-time multi-agent systems with switching to-pology
    (КПІ ім. Ігоря Сікорського, 2024) Dorofieiev, Y. I.; Lyubchyk, L. M.; Malko, M. M.
    The problem of consensus control of linear discrete-time multi-agent systems (MASs) with switching topology is considered in the presence of a leader. The goal of consensus control is to bring the states of all agents to the leader state while providing stability for local agents, as well as the MAS as a whole. In contrast to the traditional approach, which uses the concept of an extended dynamic multi-agent system model and communication topology graph Laplacian, this paper proposes a decomposition approach, which provides a separate design of local controllers. The control law is chosen in the form of distributed feedback with discrete PID controllers. The problem of local controllers’ design is reduced to a set of semidefinite programming problems using the method of invariant ellipsoids. Sufficient conditions for agents’ stabilization and global consensus condition fulfillment are obtained using the linear matrix inequality technique. The availability of information about a finite set of possible configurations between agents allows us to design local controllers offline at the design stage. A numerical example demonstrates the effectiveness of the proposed approach.