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<p>The research demonstrates a new method for predicting LC circuit frequencies through linear regression techniques applied to standard resonance formula-derived log-transformed L and C values. The modeling technique produces outstanding precision because it delivers a root mean squared error measurement of 1.51 × 10⁻¹². The observed relationship reveals that frequencies affect component measurements by -0.5 while using − 1.8379 as the intercept and matching the predictive resonance model. The performance visuals of the proposed model show minimal error in prediction across different LC combinations along with smooth and reliable predictive outputs and negligible bias according to the residual histogram. We examine different regression methods to determine the resonant frequency of LC circuits by transforming the input features into log values. It was found that Linear Regression reaches close to zero RMSE, but SVR and Random Forest show higher errors. The study proves that log-transformed linear regression works as a basic but efficient method for accurate frequency estimation in resonant LC circuits.</p>

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LC oscillator frequency prediction using machine learning linear regression algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月6日

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  • Mandar Jatkar,
  • Vasudeva G,
  • Tripti R. Kulkarni,
  • Roopa R. Kulkarni,
  • Aneesh Pandurangi

摘要

The research demonstrates a new method for predicting LC circuit frequencies through linear regression techniques applied to standard resonance formula-derived log-transformed L and C values. The modeling technique produces outstanding precision because it delivers a root mean squared error measurement of 1.51 × 10⁻¹². The observed relationship reveals that frequencies affect component measurements by -0.5 while using − 1.8379 as the intercept and matching the predictive resonance model. The performance visuals of the proposed model show minimal error in prediction across different LC combinations along with smooth and reliable predictive outputs and negligible bias according to the residual histogram. We examine different regression methods to determine the resonant frequency of LC circuits by transforming the input features into log values. It was found that Linear Regression reaches close to zero RMSE, but SVR and Random Forest show higher errors. The study proves that log-transformed linear regression works as a basic but efficient method for accurate frequency estimation in resonant LC circuits.