混沌(操作系统)
人工神经网络
过程(计算)
聚合
计算机科学
算法
生物系统
材料科学
人工智能
聚合物
复合材料
计算机安全
生物
操作系统
作者
Shuzhi Gao,Yimeng Zhang,Yimin Zhang,Guoguang Zhang
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:: 1-1
被引量:4
标识
DOI:10.1109/jsen.2020.3026550
摘要
The conversion rate of vinyl chloride monomer (VCM) is an important product quality indicator in the process of Polyvinyl chloride (PVC) polymerization. Due to the complexity of the PVC polymerization process and the limitation of site conditions, it is difficult to obtain the VCM conversion rate online in real time.Therefore, this article puts forward a soft-sensor model based on Beetle Antennae Search Algorithm (BAS) to optimize Elman neural network(Elman). Firstly, Multi-Cluster Feature Selection (MCFS) is used to reduce the dimensionality of the high-dimensional input variables, so that we get auxiliary variables of the soft-sensor model. Then, using Elman neural network as a soft-sensor model, and it is trained by the proposed optimization algorithm, which combines the chaotic map and the Beetle Antennae Search Algorithm (CBAS). The simulation results show that the model can significantly improve the prediction accuracy of the VCM conversion rate while realizing the real-time control of the PVC polymerization production process.
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