prikaz prve stranice dokumenta Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža
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undergraduate thesis
Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:466371
Petrak, Dina
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Petrak, D. (2022). Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:466371

Petrak, Dina. "Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:466371

Petrak, Dina. "Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:466371

Petrak, D. (2022). 'Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:466371

Petrak D. Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:466371

D. Petrak, "Primjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:466371

Metadata
TitlePrimjena evolucijskih algoritama za razvoj arhitekture neuronskih mreža
Title (english)Application of evolutionary algorithms for the design of neural network architectures
AuthorDina Petrak
MentorMarko Đurasević (mentor)
Committee memberMarko Đurasević (predsjednik povjerenstva)
Committee memberNikolina Frid (član povjerenstva)
Committee memberZoran Medić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U današnje vrijeme, neuronske mreže sve su veći predmet interesa. Oponašanjem funkcionalnosti i povezanosti neurona u mozgu, umjetne neuronske mreže mogu učiti te raditi predikcije. Razvijene su mnoge tehnike učenja, a u ovom je radu opisano učenje genetskim algoritmom, još poznato i kao neuroevolucija. Implementirane su dvije neuronske mreže. U prvoj je genetski algoritam korišten samo za optimizaciju težina, dok je u drugoj NEAT algoritam primijenjen za optimizaciju same arhitekture neuronske mreže. Te su neuronske mreže korištene za rješavanje problema regresije. Primjer rada neuronskih mreži prikazan je na četiri funkcije različite težine. Funkcije easy, medium i hard generirane su kao primjer funkcija različitih težina u kojima sigurno postoji ovisnost izlaza o ulazu dok za funkciju zraka ta povezanost nije jasna. Na samome kraju prikazani su dobiveni rezultati.
Abstract (english)
Today, neural networks are an emerging topic of interest. By emulating the functions and connections of neurons in the human brain, artificial neural networks are able to learn and make predictions. While many different techniques can be used in the process of learning, this work focuses on using genetic algorithms. That technique is called neuroevolution. Two neural networks were implemented. The first one had a fixed structure where only the weights were optimized by the genetic algorithm. For the second one, genetic algorithm NEAT was used to improve the architecture. Neural networks were then used to solve regression problems by making predictions of four different functions. Easy, medium and hard functions are an example of functions where the connection between outputs and inputs is certain and clear. That is not the case with the air function. Results are shown in the final part of the work.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:466371
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-15 11:03:04