Academic literature on the topic 'Оптимізація рою частинок'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Contents
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Оптимізація рою частинок.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Оптимізація рою частинок"
Dаnishevskyy, V. V., and A. M. Gaidar. "ОPTIMAL PLACEMENT OF FRICTION DAMPERS FOR THE SEISMIC PROTECTION OF FRAME BUILDINGS USING THE PARTICLE SWARM OPTIMIZATION." Bulletin of Odessa State Academy of Civil Engineering and Architecture, no. 80 (September 3, 2020): 34–42. http://dx.doi.org/10.31650/2415-377x-2020-80-34-42.
Full textДойко, Н. М. "ОЦІНКА ТРАВ'ЯНИСТОГО ПОКРИВУ СХІДНОЇ БАЛКИ У ДЕНДРОПАРКУ "ОЛЕКСАНДРІЯ" НАН УКРАЇНИ З МЕТОЮ ЙОГО ОПТИМІЗАЦІЇ." Вісті Біосферного заповідника «Асканія-Нова», no. 21 (April 14, 2021): 343–49. http://dx.doi.org/10.53904/1682-2374/2019-21/49.
Full textПисаренко, П. В., М. С. Самойлік, О. Ю. Диченко, and О. М. Руденко. "СИСТЕМА КОМПЛЕКСНОГО УПРАВЛІННЯ СФЕРОЮ ПОВОДЖЕННЯ З ТВЕРДИМИ ВІДХОДАМИ В КОНТЕКСТІ ЗБАЛАНСОВАНОГО РЕГІОНАЛЬНОГО РОЗВИТКУ." Вісник Полтавської державної аграрної академії, no. 3 (September 25, 2020): 125–34. http://dx.doi.org/10.31210/visnyk2020.03.14.
Full textМаслійов, С. В., Н. О. Коржова, І. І. Ярчук, and В. Ф. Люклянчук. "ВПЛИВ РІЗНИХ ВИДІВ МІНЕРАЛЬНОГО ЖИВЛЕННЯ НА РІСТ І РОЗВИТОК ЯЧМЕНЮ ЯРОГО В ЗОНІ СТЕПУ УКРАЇНИ." Вісник Полтавської державної аграрної академії, no. 4 (December 27, 2019): 28–35. http://dx.doi.org/10.31210/visnyk2019.04.03.
Full textКулик, Г. А., В. П. Резніченко, Н. М. Трикіна, and В. О. Малаховська. "ЕФЕКТИВНІСТЬ ЗАСТОСУВАННЯ РЕГУЛЯТОРІВ РОСТУ ПРИ ВИРОЩУВАННІ ЦУКРОВИХ БУРЯКІВ У ЦЕНТРАЛЬНІЙ УКРАЇНІ." Вісник Полтавської державної аграрної академії, no. 2 (June 26, 2020): 43–49. http://dx.doi.org/10.31210/visnyk2020.02.05.
Full textPovkhan, Igor. "ПИТАННЯ СКЛАДНОСТІ ПРОЦЕДУРИ ПОБУДОВИ СХЕМИ АЛГОРИТМІЧНОГО ДЕРЕВА КЛАСИФІКАЦІЇ." TECHNICAL SCIENCES AND TECHNOLOGIES, no. 3(21) (2020): 142–53. http://dx.doi.org/10.25140/2411-5363-2020-3(21)-142-153.
Full textDissertations / Theses on the topic "Оптимізація рою частинок"
Абдураімов, Таір Заірович. "Алгоритм глибинного аналізу даних для задачі класифікації на основі штучного бджолиного рою." Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/38328.
Full textActuality of theme. As the size of digital information grows exponentially, large amounts of raw data need to be extracted. To date, there are several methods to customize and process data according to our needs. The most common method is to use Data Mining. Data Mining is used to extract implicit, valid and potentially useful knowledge from large amounts of raw data. The knowledge gained must be accurate, readable and easy to understand. In addition, the data mining process is also called the knowledge discovery process, which has been used in most new interdisciplinary fields, such as databases, artificial intelligence statistics, visualization, parallel computing, and other fields. One of the new and extremely powerful algorithms used in Data Mining is evolutionary algorithms and swarm-based approaches, such as the ant algorithm and particle swarm optimization. In this paper, it is proposed to use a fairly new idea of the swarm of bee swarm algorithm for data mining for a widespread classification problem. Purpose: to develop an algorithm for data mining for the classification problem based on the swarm of bee swarms, which exceeds other common classifiers in terms of accuracy of results and consistency. The object of research is the process of data mining for the classification problem. The subject of the study is the use of a swarm of bee swarms for data mining. Research methods. Methods of parametric research of heuristic algorithms, and also methods of the comparative analysis for algorithms of data mining are used. The scientific novelty of the work is as follows: 1. As a result of the analysis of existing solutions for the classification problem, it is decided to use such metaheuristics as the swarm of bee swarm. 2. The implementation of the bee algorithm for data mining is proposed. The practical value of the results obtained in this work is that the developed algorithm can be used as a classifier for data mining. In addition, the proposed adaptation of the bee algorithm can be considered as a useful and accurate solution to such an important problem as the problem of data classification. Approbation of work. The main provisions and results of the work were presented and discussed at the scientific conference of undergraduates and graduate students "Applied Mathematics and Computing" PMK-2019 (Kyiv, 2019), as well as at the scientific conference of undergraduates and graduate students "Applied Mathematics and Computing" PMK-2020 (Kyiv, 2020). Structure and scope of work. The master's dissertation consists of an introduction, four chapters, conclusions and appendices. The introduction provides a general description of the work, assesses the current state of the problem, substantiates the relevance of research, formulates the purpose and objectives of research, shows the scientific novelty of the results and the practical value of the work, provides information on testing and implementation. The first section discusses the data mining algorithms used for the classification problem. The possibility of using heuristic algorithms, namely the bee swarm algorithm for this problem, is substantiated. The second section discusses in detail the algorithm of the bee swarm and the principles of its operation, also describes the proposed method of its application for data mining, namely for the classification problem. The third section describes the developed algorithm and the software application in which it is implemented. In the fourth section the estimation of efficiency of the offered algorithm, on the basis of testing of algorithm, and also the comparative analysis between the developed algorithm and already different is resulted. The conclusions present the results of the master's dissertation. The work is performed on 89 sheets, contains a link to the list of used literature sources with 18 titles. The paper presents 38 figures and 2 appendices.