MODELLING THE CHARACTERISTICS OF AN INVERTED MAGNETRON USING NEURAL NETWORKS

• Published in: Materiali in tehnologije Vol. 43; no. 2; pp. 85 - 96
• Main Author: Belie, Igor
• Format: Journal Article
• Language: Slovenian
• Published: 01.03.2009
• ISSN: 1580-2949; 1580-3414

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 CCGs.