Autors: Ivanova, M. S., Rozeva, A. G., Stoyanov S.
Title: Ensemble Machine Learning Algorithms for Analysis of Noise Characteristics in Electronic Circuits
Keywords: ensemble machine learning, electronic circuits

Abstract: Electrical noise in electronic circuits is unwanted disturbance of information-carrying signals and it occurs due to various reasons. From the different noise types thermal and flicker noise have been recognized to have the highest influence on the workability of electronic circuits. That is why they have been investigated in the current paper. For the purpose of this research, non-inverting and instrumentation amplifiers are used and their functionality in the time and frequency domains is simulated with the focus on obtaining relationship and dependency between electrical noise, gain and frequency. The data collected by the simulation are pre-processed and further on are implemented for learning models by ensemble machine learning algorithms. The established models turn out to be capable to predict output noise with high accuracy. This could be useful for designers of electronic circuits for the analysis of their functionality and behavior.

References

    Issue

    48th International Conference on Applications of Mathematics in Engineering and Economics, AMEE 2022/AIP Conference Proceedings, vol. 2939, issue 1, pp. 1-7, 2022, Bulgaria, American Institute of Physics Inc., ISBN:978-073544763-9/DOI:10.1063/5.0178663

    Copyright AIP

    Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus