论文标题
合奏长的短期内存（ENLSTM）网络
Ensemble long short-term memory (EnLSTM) network
论文作者
论文摘要
在这项研究中，我们提出了一个集合长的短期内存（ENLSTM）网络，该网络可以在小型数据集和过程顺序数据上进行训练。 ENLST是通过将集成神经网络（ENN）和级联长期记忆（C-LSTM）网络相结合来构建的，以利用其互补优势。为了解决与训练失败相关的过度连接和扰动补偿问题，由于小数据问题的性质，引入了模型参数扰动和高效率观察扰动方法。将ENLSM与已发布的数据集上的常用模型进行比较，并被证明是生成均值越（MSE）降低34％的井日志的最新模型。在案例研究中，基于日志记录时（LWD）数据生成钻孔时无法测量的12个井。 ENLST能够降低成本并节省实践时间。
In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data. The EnLSTM is built by combining the ensemble neural network (ENN) and the cascaded long short-term memory (C-LSTM) network to leverage their complementary strengths. In order to resolve the issues of over-convergence and disturbance compensation associated with training failure owing to the nature of small-data problems, model parameter perturbation and high-fidelity observation perturbation methods are introduced. The EnLSTM is compared with commonly-used models on a published dataset, and proven to be the state-of-the-art model in generating well logs with a mean-square-error (MSE) reduction of 34%. In the case study, 12 well logs that cannot be measured while drilling are generated based on logging-while-drilling (LWD) data. The EnLSTM is capable to reduce cost and save time in practice.
