Infinity-norm-based Input-to-State-Stable Long Short-Term Memory networks: a thermal systems perspective
Researchers have developed a new method to ensure the stability of Long Short-Term Memory (LSTM) networks used in system identification, particularly for nonlinear dynamical systems like thermal processes. Their approach derives a sufficient condition for Input-to-State Stability (ISS) based on the infinity-norm, which relies on fewer network parameters than previous methods. This technique was validated on a thermal system, where the ISS-promoted LSTM outperformed both physics-based models and other recurrent neural network variants. AI
IMPACT Enhances the reliability of recurrent neural networks for modeling complex dynamic systems.