APPLICATION OF MACHINE LEARNING FOR REMOTE ELECTRONICS EXPERIMENTS AS THE MEAN OF INDENTIFICATION

We are increasingly faced with automated solutions that create a dynamic and constantly evolving scientific and technical environment, with new challenges in finding more accurate and efficient solutions for identification. This article introduces a machine learning application that allows you to automatically recognize and identify learners in a distance learning experiment without being explicitly programmed. Experimental methods and components have been transferred to a distance learning laboratory environment. A conceptual model of an electronics laboratory has been developed for remote experiments using identification methods. This article focuses on machine learning using the LabVIEW package, which can access data and use it for self-study. The article presents the application of machine learning software LabVIEW National Instruments and NI ELVIS hardware simulators for electronic laboratory remote experiments and perspectives in the educational process