Electromagnetic Sensor Behavior Prediction Using LSTM Neural Network for Enhanced Accuracy and Reliable Performance

Accurate sensors are essential in industries for system monitoring and control. High-precision sensor data can enhance predictions and prevent costly errors, especially in noisy environments that require detailed analysis. In this paper, we simulate the Long Short-Term Memory (LSTM) neural network model to predict the real magnetic signal from noisy data. The LSTM algorithm effectively removes Gaussian and random noise from the received data, providing accurate predictions of the real magnetic signals. Compared to previous works that used simpler methods, such as classical filters or basic models, our proposed algorithm demonstrates significantly higher accuracy. By using LSTM to remove noise and improve predictions, errors have been minimized, resulting in enhanced prediction accuracy. The simulation results indicate that the model can simulate the real behavior of the system with 97.67% accuracy, with minimal difference between the predicted and actual signals. This level of accuracy allows our model to significantly outperform previous methods in predicting magnetic signals in noisy environments and proves highly effective for industrial applications requiring precise accuracy.