Deep Long-Short Term Memory networks: Stability properties and Experimental validation
Fabio Bonassi, Alessio La Bella, Giulio Panzani, Marcello Farina,, Riccardo Scattolini
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Abstract
The aim of this work is to investigate the use of Incrementally Input-to-State Stable (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-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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Taxonomy
TopicsFault Detection and Control Systems · Model Reduction and Neural Networks · Machine Fault Diagnosis Techniques
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory