基于自编码神经网络特征提取的回声状态网络研究及过程建模应用

朱宝; 乔俊飞

doi:10.11949/0438-1157.20191350

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基于自编码神经网络特征提取的回声状态网络研究及过程建模应用

过程系统工程 | 更新时间：2026-07-24

- 基于自编码神经网络特征提取的回声状态网络研究及过程建模应用
- Features extracted from auto-encoder based echo state network and its applications to process modeling
- 化工学报 2019年70卷第12期页码：4770-4776
- 作者机构：
  
  1.北京工业大学信息学部，北京 100124
  2.中国电建集团海外投资有限公司，北京 100048
- 作者简介：
  
  朱宝（1987—），男，博士，zhubao@powerchina.cn
  乔俊飞（1968—），男，博士，教授，junfeiq@bjut.edu.cn
- 基金信息：
  
  国家自然科学基金重大项目(61890930)
- DOI：10.11949/0438-1157.20191350
  中图分类号： TP 29
- 收稿：2019-11-05，
  
  修回：2019-11-15，
  
  网络首发：2020-06-09，
  
  纸质出版：2019-12-05
- 稿件说明：
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朱宝, 乔俊飞. 基于自编码神经网络特征提取的回声状态网络研究及过程建模应用[J]. 化工学报, 2019, 70(12): 4770-4776

ZHU Bao, QIAO Junfei. Features extracted from auto-encoder based echo state network and its applications to process modeling[J]. CIESC Journal, 2019, 70(12): 4770-4776
朱宝, 乔俊飞. 基于自编码神经网络特征提取的回声状态网络研究及过程建模应用[J]. 化工学报, 2019, 70(12): 4770-4776 DOI： 10.11949/0438-1157.20191350.

ZHU Bao, QIAO Junfei. Features extracted from auto-encoder based echo state network and its applications to process modeling[J]. CIESC Journal, 2019, 70(12): 4770-4776 DOI： 10.11949/0438-1157.20191350.

摘要

在复杂化工建模过程中，由于过程数据的时序性、高非线性以及高维数的特点，导致传统的静态神经网络建模无法满足一定的精度。为了解决该问题，提出一种基于自编码神经网络特征提取的回声状态网络模型（features extracted from auto-encoder based echo state network，FEAE-ESN）。传统回声状态网络(echo state network

ESN)方法中，储备池的节点数目很多，输出的维数很高，数据间存在共线性。为解决上述问题，待回声状态网络训练好之后，使用自编码神经网络对其储备池输出进行特征提取。通过自编码网络特征提取，一方面可以有效地降低储备池输出的维数，从而降低数据的复杂度；另一方面提取的特征去除了原有储备池输出的共线性，能够进一步提高广义逆的计算性能；最终提高回声状态网络的建模精度。所提方法FEAE-ESN用于田纳西-伊斯曼复杂过程建模，仿真结果验证了所提方法的有效性。

Abstract

In the process of complex chemical modeling

the traditional static neural network modeling can not meet certain accuracy because of the time sequence

high nonlinearity and high dimension of process data. To solve this problem

a feature extracted from auto-encoder based echo state network (FEAE-ESN) is proposed in this paper. In the traditional echo state network (ESN) method

the number of nodes in the reserve pool of ESN is large

and then the dimension of the reserve pool output is very high. Therefore

to solve this problem

the auto-encoder is used to extract features from the reserve pool output of well-trained ESN. Through the feature extraction of auto-encoder

on one hand

the dimension of the output of the reserve pool can be effectively reduced

thereby reducing the complexity of the data; on the other hand

the collinearity of the outputs of the original reserve pool can be removed through extracting features

which can further improve the calculation performance of generalized inverse. Ultimately

the modeling accuracy of ESN is improved. The proposed FEAE-ESN is applied to modeling the Tennessee-Eastman process. The simulation results verify the effectiveness of the proposed method.

关键词

Keywords

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