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Statistics and Machine Learning for Behavioral Prediction of Operational Transconductance Amplifiers with Focus on Regression Analysis

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1513)

Abstract

The goal in this paper is to present a method for predictive analysis of Operational Transconductance Amplifiers behavior in frequency domain applying regression algorithms. The exploration is performed applying linear regression, additive regression and transformed regression algorithms for solving regression tasks and linear and logistic regression for deciding classification problems. The evaluation of developed predictive models show the advantage of linear regression in comparison to others.

Keywords

  • Regression analysis
  • Operational transconductance amplifier
  • Machine learning
  • Statistics

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Acknowledgment

This research is supported by Bulgarian National Science Fund in the scope of the project “Exploration the application of statistics and machine learning in electronics” under contract number КП-06-H42/1.

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Correspondence to Malinka Ivanova .

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Ivanova, M., Petkova, P., Petkov, N. (2021). Statistics and Machine Learning for Behavioral Prediction of Operational Transconductance Amplifiers with Focus on Regression Analysis. In: Cao, W., Ozcan, A., Xie, H., Guan, B. (eds) Computing and Data Science. CONF-CDS 2021. Communications in Computer and Information Science, vol 1513. Springer, Singapore. https://doi.org/10.1007/978-981-16-8885-0_25

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  • DOI: https://doi.org/10.1007/978-981-16-8885-0_25

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