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

04/06/2023

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by Fabio Bonassi, et al.

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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.

Fabio Bonassi
7 publications
Alessio La Bella
1 publication
Giulio Panzani
2 publications
Marcello Farina
6 publications
Riccardo Scattolini
8 publications

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