Autors: Ivanova, M. S.
Title: Deep Learning at Behavioral Analysis of Analog Amplifiers with Negative Feedback
Keywords: Analog amplifiers; Behavioral predictive analysis

Abstract: In the paper an approach for behavioral analysis of analog amplifiers with negative feedback is presented. It can be in support of an engineer analyst, speeding the analytical process, reducing operational efforts and shortening the path from research laboratory to manufacturing. Behavioral analysis treats the amplifiers and feedback blocks as “black boxes” and do not interested in their internal structure and occurred electrical processes. The connections among high level amplifier building blocks and their main parameters are studied trough utilization of artificial neural networks to predict the number of stages, feedback availability and feedback type in a two-step analytical process. The performance of the created predictive models is evaluated and the obtained errors are small.

References

    Issue

    8th International Congress on Information and Communication Technology, ICICT 2023/Lecture Notes in Networks and Systems, vol. 696, pp. 855 - 863, 2024, United Kingdom, Springer Science and Business Media Deutschland GmbH, ISBN:978-981993235-1/DOI:10.1007/978-981-99-3236-8_68

    Copyright Springer, Singapore

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