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New LSTM stability method outperforms existing models

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing a new method for improving LSTM network stability. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New LSTM stability method outperforms existing models

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The cluster contains an academic paper detailing a new method for improving LSTM network stability. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Stefano De Carli, Davide Previtali, Leandro Pitturelli, Mirko Mazzoleni, Antonio Ferramosca, Fabio Previdi ·

    Infinity-norm-based Input-to-State-Stable Long Short-Term Memory networks: a thermal systems perspective

    arXiv:2503.11553v3 Announce Type: replace-cross Abstract: Recurrent Neural Networks (RNNs) have shown remarkable performances in system identification, particularly in nonlinear dynamical systems such as thermal processes. However, stability remains a critical challenge in practi…

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