| 基于自联想递阶神经网络的多输入参数化工过程软传感器(英文) |
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| 引用本文： | 贺彦林,徐圆,耿志强,朱群雄.基于自联想递阶神经网络的多输入参数化工过程软传感器(英文)[J].中国化学工程学报,2015,23(1):138-145. |
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| 作者姓名： | 贺彦林 徐圆 耿志强 朱群雄 |
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| 作者单位： | College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China(61074153) |
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| 摘 要： | To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network (AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts:groups of subnets based on well trained Auto-associative Neural Networks (AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method, the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification (EDAC) is adopted. Soft sensor using AHNN based on EDAC (EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid (PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model.
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| 关 键 词： | Soft sensor Auto-associative hierarchical neural network Purified terephthalic acid solvent system Matter-element |
| 收稿时间： | 2013-03-18 |
Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network |
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| Yanlin He;Yuan Xu;Zhiqiang Geng;Qunxiong Zhu.Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network[J].Chinese Journal of Chemical Engineering,2015,23(1):138-145. |
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| Authors: | Yanlin He;Yuan Xu;Zhiqiang Geng;Qunxiong Zhu |
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| Affiliation: | College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| Abstract: | To explore the problems of monitoring chemical processes with large numbers of input parameters, a method based on Auto-associative Hierarchical Neural Network (AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts:groups of subnets based on well trained Auto-associative Neural Networks (AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method, the subnets are designed according to the classification of the data attributes. For getting the classification, an effective method called Extension Data Attributes Classification (EDAC) is adopted. Soft sensor using AHNN based on EDAC (EDAC-AHNN) is introduced. As a case study, the production data of Purified Terephthalic Acid (PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature, which shows the efficiency of the proposed model. |
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| Keywords: | Soft sensor Auto-associative hierarchical neural network Purified terephthalic acid solvent system Matter-element |
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