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首页> 外文期刊>IEEE transactions on industrial informatics >Variational Autoencoder Bidirectional Long and Short-Term Memory Neural Network Soft-Sensor Model Based on Batch Training Strategy
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Variational Autoencoder Bidirectional Long and Short-Term Memory Neural Network Soft-Sensor Model Based on Batch Training Strategy

机译：变形AutoEncoder双向短期和短期内存神经网络软传感器模型基于批量培训策略

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摘要

Long and short-term memory (LSTM) has been used in soft-sensor modeling of industrial processes in recent years. However, LSTM still has many defects for soft-sensor. This article proposes a variational autoencoder bidirectional LSTM soft-sensor modeling method based on batch training (Bt-VAEBiLSTM). First, the training samples are divided into multiple batches according to the time series, in order to reduce the influence of abnormal points and noise, the variational autoencoder is then used to reconstruct the training samples in each batch in order to solve the problem of the global LSTM model discarding critical data information during training; this article proposes a batch training method that is to say the reconstructed samples are trained in batches according to the time series. After the training of a batch samples is completed, the structural parameters of the previous local bidirectional LSTM (BiLSTM) model are shared with the next local BiLSTM model as the initial parameters to retain important state information. At the same time, in order to prevent the Bt-VAEBiLSTM model from overfitting, the L2 regularization term is introduced in the loss function. The effectiveness of the proposed method is verified by simulation experiments on the grinding and classifying process.
机译：近年来，长期内存（LSTM）已用于工业过程的软传感器建模。但是，LSTM仍然对软传感器缺乏许多缺陷。本文提出了一种基于批量训练（BT-Vaebilstm）的变形AutoEncoder双向LSTM软传感器建模方法。首先，根据时间序列将训练样本分为多个批次，以减少异常点和噪声的影响，然后使用变分性自动码器在每批中重建训练样本以解决问题全球LSTM模型在培训期间丢弃关键数据信息;本文提出了一种批量训练方法，即根据时间序列，重建的样本批量培训。在完成批次样本的训练之后，以前的本地双向LSTM（BILSTM）模型的结构参数与下一个本地BILSTM模型共享作为初始参数以保留重要的状态信息。同时，为了防止BT-Vaebilstm模型过度拟合，L2正则化术语在损耗函数中引入。通过仿真实验验证了所提出的方法的有效性对磨削和分类过程进行了验证。

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