Deep Long-Short Term Memory networks: Stability properties and Experimental validation
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Abstract (arXiv)
The aim of this work is to investigate the use of Incrementally Input-to-State Stable (δISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems. We show that suitable sufficient conditions on the weights of the network can be leveraged to setup a training procedure able to learn provenly-δISS LSTM models from data. The proposed approach is tested on a real brake-by-wire apparatus to identify a model of the system from input-output experimentally collected data. Results show satisfactory modeling performances.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 6 Apr 2023.
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12 of 31 references.
References · 12
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2019Nonlinear System Identification: A User-Oriented Roadmapcited by 57
- 2018Multi-Step Prediction of Dynamic Systems with Recurrent Neural Networkscited by 25
- 2020On the stability properties of Gated Recurrent Units neural networkscited by 19
- 2021On Recurrent Neural Networks for learning-based control: recent results and ideas for future developmentscited by 14
- 2019LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verificationcited by 13
- 2017An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecastingcited by 12
- 2021Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networkscited by 11
- 2018Recurrent Neural Network-based Model Predictive Control for Continuous Pharmaceutical Manufacturingcited by 9
- 2020Stability of discrete-time feed-forward neural networks in NARX configurationcited by 8
- 2021Recurrent Neural Network-based Internal Model Control design for stable nonlinear systemscited by 5
- 2022An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX modelscited by 2
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