Electromagnetic Compatibility Test Signal Prediction Method Based on LSTM Network

Ensuring electromagnetic compatibility (EMC) represents a critical requirement for maintaining reliable operation of electronic systems in electromagnetic interference (EMI) environments. Current EMC testing methodologies, while essential for evaluating device resilience, often fail to encompass the full spectrum of operational scenarios due to limitations in captured frequency ranges and temporal durations. This constraint necessitates predictive modeling of unobserved electromagnetic phenomena to enable comprehensive EMC performance evaluation. Our study presents an innovative framework employing Long Short-Term Memory (LSTM) networks enhanced by Bayesian optimization (BO) for accurate prediction of EMC test signals. Empirical validation demonstrates that our optimized LSTM architecture achieves superior prediction accuracy and generalization capabilities compared to conventional neural network approaches, producing reliable outcomes when applied to laboratory-controlled EMC test datasets. These findings substantiate the method's potential for enhancing EMC testing protocols through advanced signal forecasting.