Autors: Ivanova, M. S.
Title: Methodology for Analysis of Gm-C Filters based on Statistical, Fuzzy Logic and Machine Learning Approach
Keywords: CAD, fuzzy logic, Gm-C filter, machine learning, statistical experimental design, VHDL-AMS behavioral model

Abstract: In the paper a new approach for analysis of Gm-C filters is presented that is suitable for automation of some engineering tasks and integration in CAD tools. The proposed methodology includes data gathering through simulation and circuit mathematical description, utilization of statistical experimental design technique, fuzzy logic method and machine learning algorithm. It is verified through analysis of a low pass Gm-C second order Butterworth filter and creation of several models: high level behavioral VHDL-AMS model of Gm-C active filter, statistical and fuzzy logic based model of inference and machine learning analytical model.



    EEET '20: Proceedings of the 2020 3rd International Conference on Electronics and Electrical Engineering Technology, ACM International Conference Proceeding Series, pp. 80-86, 2020, Japan, ACM Inc., ISBN: 978-145038756-9,

    Copyright ACM Inc.

    Цитирания (Citation/s):
    1. Grethler M., Marinov M.B., Klumpp V. (2021) Embedded Machine Learning for Machine Condition Monitoring. In: Perakovic D., Knapcikova L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 382. Springer, Cham. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus