化工学报2019，Vol.70Issue(12)：4770-4776,7.DOI:10.11949/0438-1157.20191350
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
摘要
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
自编码神经网络/回声状态网络/特征提取/软测量/过程建模Key words
auto-encoder/ echo state network/ feature extraction/ soft sensor/ process modeling分类
信息技术与安全科学引用本文复制引用
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):4770-4776,7.基金项目
国家自然科学基金重大项目(61890930) （61890930）
