21 KB494 KB46 KB200020242009Belič, Igorštevilka:2letnik:43str. 85-95ISSN:1580-2949COBISSID:723882URN:URN:NBN:SI:doc-71PS43V0enInštitut za kovinske materiale in tehnologijeMateriali in tehnologijekalibriranjemagnetronimodeliranjenevronski sistemivakuumska fizikaModelling the characteristics of an inverted magnetron using neural networks| Modeliranje karakteristike invertnega magnetrona z nevronskimi sistemi|The inverted magnetron or cold cathode gauge (CCG) is a device used as a vacuum gauge. It is a very robust device, with mostly very positive properties. The problem with its use lies in its nonlinear, temporary, variable characteristic and the fact that the theory of its operation is not thoroughly understood. Neural networks are, therefore, an ideal solution for building a nonlinear characteristics model, based on a set of measured points. Such a model is valid for some certain period of time. When the characteristic of the CCG is altered significantly (due to aging and contamination), the process of recalibration needs to be done, where again neural networks provide a very easy-to-use and robust tool. In the article the simulation of the CCG characteristics is presented. It is meant to provide sufficiently large sets of data to enable a study of the modelling properties of the used neural networks. The CCG characteristic was split into several segments, each of which was modelled by a separate neural network. The results of the study are presented. The study ended in a practically usable methodology for employing neural networks to calibrate (or recalibrate) the CCGsInvertni magnetron ali merilnik s hladno katodo (CCG) je naprava, ki se uporablja kot grobi merilnik tlaka v vakuumskih sistemih. To so robustne naprave s celo vrsto dobrih lastnosti. Problem praktične uporabe je, da je karakteristika CCG zelo nelinearna, časovno spremenljiva in da teorija delovanja ni povsem znana. Zato so nevronski sistemi idealno orodje za gradnjo nelinearnega modela, ki je zgrajen na množici izmerjenih točk. Tak model je uporaben v nekem časovnem obdobju. Ko se karakteristika CCG preveč spremeni (zaradi staranja in kontaminacije naprave), je treba narediti rekalibracijo. Tudi pri rekalibraciji so nevronski sistemi uporabljeni kot orodje, ki je robustno in enostavno za uporabo. V prispevku je opisana simulacija karakteristike CCG. Namenjena je generiranju zadostnega števila točk, ki so omogočile študijo lastnosti modeliranja z nevronskimi sistemi. Celotna karakteristika CCG je bila razdeljena na nekaj segmentov, pri čemer je bil vsak segment posebej modeliran s svojim nevronskim sistemom. Predstavljeni so rezultati študije. Rezultat študije je praktično uporabna metodologija modeliranja karakteristike CCG z nevronskimi sistemi, ki jih uporabimo za kalibracijo (rekalibracijo) merilnikaTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaInštitut za kovinske materiale in tehnologije