基于深度置信网络的4-CBA软测量建模

PTA工业生产过程中4-CBA的含量是评价其产品质量的重要依据。将深度置信网络和已有的浅层算法相结合，提出基于深度置信网络的4-CBA软测量模型。深度置信网络是一种典型的深度学习算法，该算法在特征学习方面优势显著。根据实验结果，基于深度置信网络的软测量模型能够很好地估计4-CBA含量，和单纯的BP神经网络模型相比，基于深度置信网络的模型预测精度更高。

Soft sensor modeling of 4-CBA based on deep belief networks

In industrial PTA production process, 4-CBA concentration is the important basis of PTA product quality evaluation. This paper combining the deep belief networks and BP neural networks proposes a soft sensor model of 4-CBA based on deep belief networks. Deep belief network is one kind of typical deep learning algorithm. The algorithm has remarkable superiority in feature learning. According to experimental results, a soft sensor model based on deep belief networks can predict 4-CBA concentration well. Compared with BP neural network model, the model based on deep belief networks has higher prediction precision.