多元统计
时间序列
系列(地层学)
深度学习
计算机科学
贝叶斯网络
残余物
机器学习
卷积神经网络
数据挖掘
数据建模
人工神经网络
人工智能
算法
数据库
生物
古生物学
作者
Yuantao Yao,Minghan Yang,Qianqian Wang,Min Xie
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:19 (2): 1977-1987
被引量:9
标识
DOI:10.1109/tii.2022.3198670
摘要
With the rapid progress of the industrial Internet of Things (IIoT), reducing data uncertainty has become a critical issue in predicting the development trends of systems and formulating future maintenance strategies. This article proposes an end-to-end, deep hybrid network-based, short-term, multivariate time-series prediction framework for industrial processes. First, the maximal information coefficient is adopted to extract the nonlinear variate correlation features. Second, a convolutional neural network with a residual elimination module is designed to eliminate data uncertainty. Third, a bidirectional gated recurrent unit network is connected in a time-distributed form to achieve step-ahead prediction. Last, an optimized Bayesian optimization method is adopted to optimize the model's learning rate. A comparison with other state-of-the-art, deep learning-based, time-series prediction methods in the case study illustrates the superiority of the proposed framework in noisy IIoT environments.
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