Deep Long-Short Term Memory networks: Stability properties and Experimental validation

• URL: http://arxiv.org/abs/2304.02975v1
• Date: Thu, 6 Apr 2023 10:02:17 GMT
• Title: Deep Long-Short Term Memory networks: Stability properties and Experimental validation
• Authors: Fabio Bonassi, Alessio La Bella, Giulio Panzani, Marcello Farina, Riccardo Scattolini
• Abstract summary: We show that suitable sufficient conditions on the weights of the network can be leveraged to setup a training procedure able to learn provenly-$delta$ISS 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.
• Score: 0.20999222360659603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) 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-$\delta$ISS 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.

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