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.

翻译：本研究旨在探讨增量输入-状态稳定（$\delta$ISS）深度长短期记忆网络（LSTM）在非线性动力系统辨识中的应用。我们证明了网络权重的合适充分条件可用于建立训练流程，从而能够从数据中学习得到经证明具有$\delta$ISS特性的LSTM模型。所提方法在实际线控制动装置上进行了测试，基于实验采集的输入-输出数据对系统进行模型辨识。结果表明该方法具有令人满意的建模性能。