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Processes soft modeling based on stacked autoencoders and wavelet extreme learning machine for aluminum plant-wide application
Control Engineering Practice ( IF 4.9 ) Pub Date : 2021-01-06 , DOI: 10.1016/j.conengprac.2020.104706
Yongxiang Lei , Hamid Reza Karimi , Lihui Cen , Xiaofang Chen , Yongfang Xie

Data-driven soft modeling has been extensively used for industrial processes to estimate key quality indicators which are hard to measure by some physical devices. However,the existing deep soft methods faces the challenge of training efficiency, gradient diminishing and explosion. Constructing an accurate and robust soft model is still a challenging topic from an application point of view. This paper develops an effective and efficient soft method (SAE-WELM) for processes modeling. First, a stacked autoencoder (SAE) is used to extract the deep features. Then, a top-layer extreme learning machine (ELM) is further applied to a plant-wide industrial aluminum production process. The activation function is wavelet kernel. Finally, the approximation and convergence of the proposed SAE-WELM are theoretically proved. The industrial case demonstrates that SAE-WELM captures the deep features faster than other iterative-based neural networks, and the accuracy and robustness outperform the existing state-of-the-art methods.



中文翻译:

基于堆叠式自动编码器和小波极限学习机的软模型处理在铝厂范围内的应用

数据驱动的软建模已广泛用于工业过程中,以估算关键质量指标,而某些物理设备难以衡量这些指标。然而,现有的深层软方法面临训练效率,梯度递减和爆炸的挑战。从应用程序的角度来看,构建准确而强大的软模型仍然是一个具有挑战性的主题。本文开发了一种有效且高效的软件建模方法(SAE-WELM)。首先,使用堆叠式自动编码器(SAE)提取深度特征。然后,将顶层极限学习机(ELM)应用于工厂范围的工业铝生产过程。激活函数是小波核。最后,从理论上证明了所提出的SAE-WELM的逼近和收敛性。

更新日期:2021-01-06
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