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Apr, 2023
深度长短时记忆网络：稳定性质和实验验证
Deep Long-Short Term Memory networks: Stability properties and Experimental validation
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Fabio Bonassi, Alessio La Bella, Giulio Panzani, Marcello Farina, Riccardo Scattolini
TL;DR
探究使用增量输入状态稳定化深度循环神经网络来识别非线性动态系统，提出了可学习被证明为增量输入状态稳定化的LSTM模型训练方法，并在实际制动器系统的输入输出实验数据中进行测试，结果表明建立的模型性能良好。
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
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