Long Short-Term Memory (LSTM) networks excel in predicting temporal data such as stock prices and physiological signals by effectively modeling the temporal dependencies in historical data, allowing for robust forecasting in dynamic model.

Accurate forecasting, especially in the context of precise and rapid motor control, holds substantial importance, influencing the efficiency of motorized systems significantly. 

We utilize LSTM for the motorized applications, such as motion compensation of the involuntary hand tremor. The precise motion prediction offered by forecasting methodologies becomes pivotal for maintaining control performance.

Motion Prediction and Controller by LSTM

Motion prediction of physiological signals such as hand tremor

LSTM-based Precise Motor Controller