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master's thesis
Primjena evolucjiskog algoritma za učenje neuronske mreže
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. urn:nbn:hr:235:466283
Medovka, Krešimir
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

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Medovka, K. (2022). Primjena evolucjiskog algoritma za učenje neuronske mreže (Master's thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:466283

Medovka, Krešimir. "Primjena evolucjiskog algoritma za učenje neuronske mreže." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:466283

Medovka, Krešimir. "Primjena evolucjiskog algoritma za učenje neuronske mreže." Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:235:466283

Medovka, K. (2022). 'Primjena evolucjiskog algoritma za učenje neuronske mreže', Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 29 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:466283

Medovka K. Primjena evolucjiskog algoritma za učenje neuronske mreže [Master's thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2022 [cited 2024 November 29] Available at: https://urn.nsk.hr/urn:nbn:hr:235:466283

K. Medovka, "Primjena evolucjiskog algoritma za učenje neuronske mreže", Master's thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:235:466283

Metadata
TitlePrimjena evolucjiskog algoritma za učenje neuronske mreže
Title (english)Application of an evolutionary algorithm for neural network learning
AuthorKrešimir Medovka
MentorPetar Ćurković (mentor)
Committee memberDamir Markučič (predsjednik povjerenstva)
Committee memberDarko Landek (član povjerenstva)
Committee memberPetar Ćurković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2022-07-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
Tema ovog diplomskog rada bila je Primjena evolucijskog algoritma za učenje neuronske mreže. Opisane su osnove neuronskih mreža i njihove karakteristike kao što su čvorovi i slojevi, te vrste neuronskih mreža. Također opisani su evolucijski algoritmi, inspiracija, struktura algoritma i potrebni koraci. Dan je opis i pregled područja NeuroEvolucije s naglaskom na Neuronske mreže evoluirajućih topologija i težina (eng. Topology and Weight Evolving Artificial Neural Networks - TWEANN) te su postavljena neka vrlo bitna pitanja koje će NEAT algoritam pokušati riješiti. Proučen je teorijski koncept NeuroEvolucije promjenjivih topologija (eng. NeuroEvolution of Augmenting Topologies - NEAT). NEAT algoritam je implementiran u programskom okruženju MATLAB te je implementacija ispitana na dva problema: XOR problem i problem stabilizacije inverznog njihala.
Abstract (english)
The topic of this thesis was Application of an evolutionary algorithm for neural network learning. The basics of neural networks and their characteristics such as nodes and layers, and types of neural networks are described. Evolutionary algorithms, inspiration, algorithm structure and necessary steps are also described. A description and overview of the field of NeuroEvolution with an emphasis on Topology and Weight Evolving Artificial Neural Networks (TWEANN) was given, and some very important questions were asked that the NEAT algorithm will try to solve. The theoretical concept of NeuroEvolution of Augmenting Topologies (NEAT) was studied. The NEAT algorithm was implemented in the MATLAB programming environment, and the implementation was tested on two problems: the XOR problem and the inverse pendulum stabilization problem.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:466283
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka strojarstva (magistar/magistra inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2022-07-17 21:15:49