Deep Long-Short Term Memory networks: Stability properties and Experimental validation
This work addresses system identification for control applications, but it is incremental as it builds on existing LSTM stability concepts.
The authors tackled the problem of identifying nonlinear dynamical systems by using deep Long Short-Term Memory networks with Incrementally Input-to-State Stable properties, achieving satisfactory modeling performances on a real brake-by-wire apparatus.
The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($δ$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-$δ$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.