基于TVA-TCN的制造过程关键参数多步预测方法

制造过程关键参数的准确预测对制造过程的精确控制起关键作用,现有预测方法通常未考虑时间动态特性,多步预测性能不佳,无法满足制造过程实际需求.对此,提出一种基于时变注意力时间卷积网络(TVA-TCN)的制造过程关键参数多步预测方法.首先,鉴于普通卷积网络感受野的局限性,利用多通道时间卷积网络提取数据的长期依赖关系,并使用Softplus激活函数降低对数据异常值的敏感度;其次,提出一种时变模型结构,通过提取上一时间步的隐藏层信息和输出信息,使得模型不仅能够随时间动态更新,而且可以缓解梯度消失,从而提高多步预测性能;最后,利用食品加工制造过程的实际数据进行多步预测实验,结果表明所提出方法与传统的方法相比具有明显的优势.

Multi-step prediction method for key parameters of manufacturing process based on TVA-TCN

Accurate prediction of key parameters in tobacco primary processing plays a key role in its precise optimization and control. Existing prediction methods usually do not consider time dynamic characteristics, and the performance of multi-step prediction is not good, which cannot meet the actual index needs of tobacco primary processing. In response to the above problems, a multi-step prediction method for key parameters of tobacco primary processing based on the time-varying attention-temporal convolutional network (TVA-TCN) is proposed. Firstly, for the key information in the input variables, the attention mechanism is introduced to capture the information. Then, a multi-channel temporal convolutional network is used to extract the long-term dependence of the data. Finally, by extracting the hidden layer information and output information of the previous time step, the model can be dynamically updated over time, thereby improving the performance of multi-step prediction. Multi-step prediction experiments are carried out using real data of tobacco primary processing, and the results show that the proposed method has obvious advantages compared with traditional methods.