prikaz prve stranice dokumenta Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije
Access restricted to students and staff of home institution
master's thesis
Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije
2021. urn:nbn:hr:190:722743
Cuculić, Mateo
University of Rijeka
Faculty of Engineering
Department of Automation and Electronics
Section of Electronics, Robotics and Automation

Cite this document

Cuculić, M. (2021). Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije (Master's thesis). Rijeka: University of Rijeka, Faculty of Engineering. Retrieved from https://urn.nsk.hr/urn:nbn:hr:190:722743

Cuculić, Mateo. "Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije." Master's thesis, University of Rijeka, Faculty of Engineering, 2021. https://urn.nsk.hr/urn:nbn:hr:190:722743

Cuculić, Mateo. "Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije." Master's thesis, University of Rijeka, Faculty of Engineering, 2021. https://urn.nsk.hr/urn:nbn:hr:190:722743

Cuculić, M. (2021). 'Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije', Master's thesis, University of Rijeka, Faculty of Engineering, accessed 24 December 2024, https://urn.nsk.hr/urn:nbn:hr:190:722743

Cuculić M. Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije [Master's thesis]. Rijeka: University of Rijeka, Faculty of Engineering; 2021 [cited 2024 December 24] Available at: https://urn.nsk.hr/urn:nbn:hr:190:722743

M. Cuculić, "Predviđanje kritične temperature supervodiča primjenom umjetne inteligencije", Master's thesis, University of Rijeka, Faculty of Engineering, Rijeka, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:190:722743

Please login to the repository to save this object to your list.
Metadata
TitlePredviđanje kritične temperature supervodiča primjenom umjetne inteligencije
Title (english)Prediction of superconductors critical temperature using artificial intelliigence methods
AuthorMateo Cuculić
MentorZlatan Car (mentor)
Committee memberRene Prenc (predsjednik povjerenstva)
Committee memberZlatan Car (član povjerenstva)
Committee memberDario Matika (član povjerenstva)
GranterUniversity of Rijeka
Faculty of Engineering
(Department of Automation and Electronics)
(Section of Electronics, Robotics and Automation)
Rijeka
Defense date and country2021-03-22, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
U ovom radu opisuje se primjena umjetnih neuronskih mreža i genetskog programiranja za predviđanje kritične temperature supravodičkih materijala. Prvo su ukratko opisani supravodiči i neka njihova svojstva. Zatim su opisane umjetne neuronske mreže, korelacijski koeficijenti i genetsko programiranje zajedno s ostalim algoritmima i bazom podataka koji se koriste u sklopu rada. Na kraju je objašnjen programski kod te su prikazani dobiveni rezultati nekoliko različitih umjetnih neuronskih mreža i programa dobivenih primjenom genetskog programiranja. Programski kod pisan je u Python programskom jeziku uz pomoć Pandas, Numpy, Seaborn, Sklearn i Gplearn programskih knjižnica.
Abstract (english)
This paper describes the application of artificial neural networks and genetic programming algorithms used for predicting the critical temperatures of superconducting materials. First, some basic properties of superconductors are briefly described. Artificial neural networks, correlation coefficients and genetic programming, along with other machine learning algorithms and the database that are used in this paper are also described. Finally, the program code is explained and the obtained results of several different artificial neural networks and genetic programs are presented. Program code is written in Python programming language with the help of pandas, Numpy, Seaborn, Sklearn and Gplearn programing libraries.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:190:722743
Study programmeTitle: Graduate University Study of Electrical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2021-10-06 08:05:16