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Updated: Jan 13, 2026

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation
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Published on: May 2, 2016

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具有集成梯度的循环神经网络 解释如何预测形状记忆合金的歇斯底里行为

Dmytro Tymoshchuk1, Oleh Yasniy1, Iryna Didych2

  • 1Department of Artificial Intelligence Systems and Data Analysis, Ternopil Ivan Puluj National Technical University, 46001 Ternopil, Ukraine.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
概括

这项研究使用反复的神经网络来预测形状记忆合金歇斯底里,实现高精度. LSTM网络展示了卓越的预测和推断能力,证实了模型的物理可信性.

关键词:
在这里,GRU GRU GRU的 LSTM 标志.一个RNN RNN在此期间,SMA SMA SMA在XAI,XAI就是XAI.循环负荷是指循环负荷.这是歇斯底里症.集成梯度的综合梯度.机器学习是机器学习.神经网络的神经网络的神经网络

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