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M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers

Author

Listed:
  • Malinka Ivanova

    (Department of Informatics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, 1797 Sofia, Bulgaria)

  • Mariana Durcheva

    (Department of Informatics, Faculty of Applied Mathematics and Informatics, Technical University of Sofia, 1797 Sofia, Bulgaria
    Department of Mathematics, Shamoon College of Engineering, Ashdod 77245, Israel)

Abstract

The design of analog circuits is a complex and repetitive process aimed at finding the best design variant. It is characterized by uncertainty and multivariate approaches. The designer has to make different choices to satisfy a predefined specification with required parameters. This paper proposes a method for facilitating the design of analog amplifiers based on m-polar fuzzy graphs theory and deep learning. M-polar fuzzy graphs are used because of their flexibility and the possibility to model different real-life multi-attribute problems. Deep learning is applied to solve a regression task and to predict the membership functions of the m-polar fuzzy graph vertices (the solutions), taking on the role of domain experts. The performance of the learner is high since the obtained errors are very small: Root Mean Squared Error is from 0.0032 to 0.0187, Absolute Error is from 0.022 to 0.098 and Relative Error is between 0.27% and 1.57%. The proposed method is verified through the design of three amplifiers: summing amplifier, subtracting amplifier, and summing/subtracting amplifier. The method can be used for improving the design process of electronic circuits with the possibility of automating some tasks.

Suggested Citation

  • Malinka Ivanova & Mariana Durcheva, 2023. "M-Polar Fuzzy Graphs and Deep Learning for the Design of Analog Amplifiers," Mathematics, MDPI, vol. 11(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1001-:d:1070003
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    References listed on IDEAS

    as
    1. Muhammad Javaid & Agha Kashif & Tabasam Rashid, 2020. "Hesitant Fuzzy Graphs and Their Products," Fuzzy Information and Engineering, Taylor & Francis Journals, vol. 12(2), pages 238-252, April.
    2. M. I. Dieste-Velasco & M. Diez-Mediavilla & C. Alonso-Tristán, 2018. "Regression and ANN Models for Electronic Circuit Design," Complexity, Hindawi, vol. 2018, pages 1-9, July.
    3. Sovan Samanta & Madhumangal Pal, 2013. "Fuzzy k-competition graphs and p-competition fuzzy graphs," Fuzzy Information and Engineering, Springer, vol. 5(2), pages 191-204, June.
    4. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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