To incorporate sparsity knowledge as well as measurement uncertainties in the traditional long short-term memory (LSTM) neural networks, an efficient relevance vector machine algorithm is introduced to the network architecture. The proposed scheme automatically determines relevant neural connections and adapts accordingly, in contrast to the classical LSTM solution. Due to its flexibility, the new LSTM scheme is less prone to overfitting and hence can approximate time-dependent solutions by use of a smaller data set. On a structural nonlinear finite element application, we show that the self-regulating framework does not require prior knowledge of a suitable network architecture and size, while ensuring satisfying accuracy at reasonable computational cost.

中文翻译：

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长期、短期相关性学习

为了将稀疏性知识和测量不确定性纳入传统的长短期记忆（LSTM）神经网络中，在网络架构中引入了有效的相关向量机算法。与经典的 LSTM 解决方案相比，所提出的方案自动确定相关的神经连接并进行相应的调整。由于其灵活性，新的 LSTM 方案不太容易出现过度拟合，因此可以通过使用较小的数据集来近似与时间相关的解决方案。在结构非线性有限元应用中，我们表明自调节框架不需要合适的网络架构和大小的先验知识，同时确保以合理的计算成本满足精度。