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master's thesis
Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa
2014. urn:nbn:hr:200:075109
Šojo, Robert
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Computer Engineering and Automation
Chair of Automation and Robotics

Cite this document

Šojo, R. (2014). Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:075109

Šojo, Robert. "Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2014. https://urn.nsk.hr/urn:nbn:hr:200:075109

Šojo, Robert. "Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2014. https://urn.nsk.hr/urn:nbn:hr:200:075109

Šojo, R. (2014). 'Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 10 December 2024, https://urn.nsk.hr/urn:nbn:hr:200:075109

Šojo R. Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2014 [cited 2024 December 10] Available at: https://urn.nsk.hr/urn:nbn:hr:200:075109

R. Šojo, "Projektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2014. Available at: https://urn.nsk.hr/urn:nbn:hr:200:075109

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Metadata
TitleProjektiranje RBF neuronske mreže za upravljanje višestrukim modelom procesa
Title (english)Design of RBF Neural Network for Process Multiple Model Control
AuthorRobert Šojo
MentorDražen Slišković (mentor)
MentorRatko Grbić (mentor)
Committee memberRobert Cupec (predsjednik povjerenstva)
Committee memberDražen Slišković (član povjerenstva)
Committee memberEmmanuel Karlo Nyarko (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Computer Engineering and Automation)
(Chair of Automation and Robotics)
Osijek
Defense date and country2014-10-17, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Process Computing
Abstract
Kako bi se estimirala teško-mjerljiva procesna veličina potrebno je raspolagati modelom procesa. Procesi su po svojoj prirodi nelinearni, a izgradnja nelinearnog modela i njihova implementacija je bitno složenija u odnosu na linearne modele. S obzirom na pretpostavku da se nelinearni proces u određenom dijelu radnog područja može dovoljno dobro opisati s linearnim modelom, cijelo radno područje procesa se rastavlja na nekoliko manjih dijelova, kako bi se vladanje procesa opisalo s više linearnih modela. Za tu svrhu koriste se metode klasteriranja podataka, kojima se od ulaznog skupa mjernih podataka, prikupljenih kroz duže vremensko razdoblje stvaraju manje grupe podataka, koje se zovu klasteri. Svrha grupiranja podataka leži u tome da se dobiju klasteri koji sadrže međusobno slične podatke, koji su međusobno različiti nasuprot podataka koji su smješteni u druge klastere. U slučaju nailaska novog mjernog uzorka za koji se treba odrediti izlaz modela procesa potrebno je omogućiti aktivaciju odgovarajućeg modela iz ovog skupa linearnih modela, čime je ovaj višestruki model upravljan. U ovom radu upravljanje višestrukim modelom procesa obavlja se RBF neuronskom mrežom čiji se broj neurona i receptivna polja pojedinog neurona određuju predstavljenim metodama klasteriranja. Ako testni podaci pripadnu samo jednom linearnom modelu, izlaz RBF neuronske mreže predstavlja izlaz tog linearnog modela. U slučaju da testni podaci pripadaju u više linearnih modela, tada bi se koristilo meko određivanje težina za svaki linearni model, a iznos težine ovisi o raspodjeli testnih podataka unutar klastera. Ovako svi linearni modeli imaju doprinos u estimaciji teško-mjerljive veličine. Ovi modeli opisuju vladanje procesa u pojedinom dijelu radnog područja.
Abstract (english)
In order to estimate hard-measured process, it is necessary to have a model of the process. The processes are by their nature non-linear and building non-linear models and their implementation is rather more complicated compared to linear models. Given the assumption that the nonlinear process in a certain part of the work area can be sufficiently well described by a linear model, the entire operating range of the process breaks down into several smaller parts in order to describe the process with multiple linear models. For this purpose data clustering method are used for creating smaller groups of data which are called clusters. Each cluster contains similar data, which are different from other data that are located in other clusters. If a new measurement sample arrives, for which is needed to determine the output of the process, it is necessary to allow the activation of appropriate model from this set of linear models. In this paper, the management of multiple process model is done by RBF neural network whose number of neurons and receptive fields of each neuron is determined by presented clustering methods. If the test data belongs to a single linear model, the output of RBF neural network represents the output of that linear model. In case that the test data belongs to the several linear models, then the soft determination of weights is used for each linear model and the amount of weight depends on the distribution of test data within a cluster. In that case all linear models have a contribution to the estimation of hard-measured value. These models describe the behavior of the process in a particular part of the work area.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:075109
Study programmeTitle: Graduate University Study Programme in Computer Engineering
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2016-08-02 09:38:10