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Regression and ANN Models for Electronic Circuit Design

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  • M. I. Dieste-Velasco
  • M. Diez-Mediavilla
  • C. Alonso-Tristán

Abstract

This paper presents a methodology to design and to predict the behaviour of electronic circuits, which combines artificial neural networks and design of experiments. This methodology can be used to model output variables in electronic circuits either with similar features to the circuit configuration that is analysed in this study or with more complex configurations in order to improve the process of electronic circuit design.

Suggested Citation

  • 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.
    • Handle: RePEc:hin:complx:7379512
    DOI: 10.1155/2018/7379512
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    References listed on IDEAS

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    1. Peng, Huaiwu & Liu, Fangrui & Yang, Xiaofeng, 2013. "A hybrid strategy of short term wind power prediction," Renewable Energy, Elsevier, vol. 50(C), pages 590-595.
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    Cited by:

    1. 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.

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